A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5

Deep-learning-based object detection algorithms have significantly improved the performance of wheat spike detection. However, UAV images crowned with small-sized, highly dense, and overlapping spikes cause the accuracy to decrease for detection. This paper proposes an improved YOLOv5 (You Look Only Once)-based method to detect wheat spikes accurately in UAV images and solve spike error detection and miss detection caused by occlusion conditions. The proposed method introduces data cleaning and data augmentation to improve the generalization ability of the detection network. The network is rebuilt by adding a microscale detection layer, setting prior anchor boxes, and adapting the confidence loss function of the detection layer based on the IoU (Intersection over Union). These refinements improve the feature extraction for small-sized wheat spikes and lead to better detection accuracy. With the confidence weights, the detection boxes in multiresolution images are fused to increase the accuracy under occlusion conditions. The result shows that the proposed method is better than the existing object detection algorithms, such as Faster RCNN, Single Shot MultiBox Detector (SSD), RetinaNet, and standard YOLOv5. The average accuracy (AP) of wheat spike detection in UAV images is 94.1%, which is 10.8% higher than the standard YOLOv5. Thus, the proposed method is a practical way to handle the spike detection in complex field scenarios and provide technical references for field-level wheat phenotype monitoring.

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Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning

Drones ◽

10.3390/drones5030066 ◽

2021 ◽

Vol 5 (3) ◽

pp. 66

Author(s):

Rahee Walambe ◽

Aboli Marathe ◽

Ketan Kotecha

Keyword(s):

Object Detection ◽

Transfer Learning ◽

Data Augmentation ◽

Test Time ◽

Complex Task ◽

Open Domain ◽

End User ◽

Aerial Vehicle ◽

Uav Images ◽

Voting Strategy

Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding challenge in the field of computer vision. Specifically, object detection in drone images is a complex task due to objects of various scales such as humans, buildings, water bodies, and hills. In this paper, we present an implementation of ensemble transfer learning to enhance the performance of the base models for multiscale object detection in drone imagery. Combined with a test-time augmentation pipeline, the algorithm combines different models and applies voting strategies to detect objects of various scales in UAV images. The data augmentation also presents a solution to the deficiency of drone image datasets. We experimented with two specific datasets in the open domain: the VisDrone dataset and the AU-AIR Dataset. Our approach is more practical and efficient due to the use of transfer learning and two-level voting strategy ensemble instead of training custom models on entire datasets. The experimentation shows significant improvement in the mAP for both VisDrone and AU-AIR datasets by employing the ensemble transfer learning method. Furthermore, the utilization of voting strategies further increases the 3reliability of the ensemble as the end-user can select and trace the effects of the mechanism for bounding box predictions.

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Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection

Sensors ◽

10.3390/s18103415 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3415 ◽

Cited By ~ 2

Author(s):

Jinpeng Zhang ◽

Jinming Zhang ◽

Shan Yu

Keyword(s):

Object Detection ◽

Sampling Method ◽

Ground Truth ◽

Detection Accuracy ◽

Two Stage ◽

Baseline Model ◽

Detection Algorithms ◽

Imbalance Problem ◽

Image Object Detection ◽

Image Object

In the image object detection task, a huge number of candidate boxes are generated to match with a relatively very small amount of ground-truth boxes, and through this method the learning samples can be created. But in fact the vast majority of the candidate boxes do not contain valid object instances and should be recognized and rejected during the training and evaluation of the network. This leads to extra high computation burden and a serious imbalance problem between object and none-object samples, thereby impeding the algorithm’s performance. Here we propose a new heuristic sampling method to generate candidate boxes for two-stage detection algorithms. It is generally applicable to the current two-stage detection algorithms to improve their detection performance. Experiments on COCO dataset showed that, relative to the baseline model, this new method could significantly increase the detection accuracy and efficiency.

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Deep Learning Based Active Monitoring for Anti-collision between Vessels and Bridges

IABSE Symposium, Guimarães 2019: Towards a Resilient Built Environment Risk and Asset Management ◽

10.2749/guimaraes.2019.0487 ◽

2019 ◽

Author(s):

Limu Chen ◽

Ye Xia ◽

Dexiong Pan ◽

Chengbin Wang

Keyword(s):

Decision Making ◽

Deep Learning ◽

Object Detection ◽

Large Scale ◽

Data Augmentation ◽

Information Support ◽

Single Shot ◽

Active Monitoring ◽

Detection Model ◽

Comparison Results

<p>Deep-learning based navigational object detection is discussed with respect to active monitoring system for anti-collision between vessel and bridge. Motion based object detection method widely used in existing anti-collision monitoring systems is incompetent in dealing with complicated and changeable waterway for its limitations in accuracy, robustness and efficiency. The video surveillance system proposed contains six modules, including image acquisition, detection, tracking, prediction, risk evaluation and decision-making, and the detection module is discussed in detail. A vessel-exclusive dataset with tons of image samples is established for neural network training and a SSD (Single Shot MultiBox Detector) based object detection model with both universality and pertinence is generated attributing to tactics of sample filtering, data augmentation and large-scale optimization, which make it capable of stable and intelligent vessel detection. Comparison results with conventional methods indicate that the proposed deep-learning method shows remarkable advantages in robustness, accuracy, efficiency and intelligence. In-situ test is carried out at Songpu Bridge in Shanghai, and the results illustrate that the method is qualified for long-term monitoring and providing information support for further analysis and decision making.</p>

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Evaluation of In Vivo Spike Detection Algorithms for Implantable MTA Brain—Silicon Interfaces

Journal of Low Power Electronics and Applications ◽

10.3390/jlpea10030026 ◽

2020 ◽

Vol 10 (3) ◽

pp. 26

Author(s):

Mattia Tambaro ◽

Elia Arturo Vallicelli ◽

Gerardo Saggese ◽

Antonio Strollo ◽

Andrea Baschirotto ◽

...

Keyword(s):

Energy Operator ◽

Oxide Semiconductor ◽

Detection Accuracy ◽

Noise Power ◽

Spike Detection ◽

Power Budget ◽

Detection Algorithms ◽

Implanted Devices

This work presents a comparison between different neural spike algorithms to find the optimum for in vivo implanted EOSFET (electrolyte–oxide-semiconductor field effect transistor) sensors. EOSFET arrays are planar sensors capable of sensing the electrical activity of nearby neuron populations in both in vitro cultures and in vivo experiments. They are characterized by a high cell-like resolution and low invasiveness compared to probes with passive electrodes, but exhibit a higher noise power that requires ad hoc spike detection algorithms to detect relevant biological activity. Algorithms for implanted devices require good detection accuracy performance and low power consumption due to the limited power budget of implanted devices. A figure of merit (FoM) based on accuracy and resource consumption is presented and used to compare different algorithms present in the literature, such as the smoothed nonlinear energy operator and correlation-based algorithms. A multi transistor array (MTA) sensor of 7 honeycomb pixels of a 30 μm2 area is simulated, generating a signal with Neurocube. This signal is then used to validate the algorithms’ performances. The results allow us to numerically determine which is the most efficient algorithm in the case of power constraint in implantable devices and to characterize its performance in terms of accuracy and resource usage.

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Ship Detection Based on YOLOv2 for SAR Imagery

Remote Sensing ◽

10.3390/rs11070786 ◽

2019 ◽

Vol 11 (7) ◽

pp. 786 ◽

Cited By ~ 41

Author(s):

Yang-Lang Chang ◽

Amare Anagaw ◽

Lena Chang ◽

Yi Wang ◽

Chih-Yu Hsiao ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Real Time ◽

Experimental Results ◽

Detection Methods ◽

Computational Time ◽

Detection Accuracy ◽

Single Shot ◽

Ship Detection ◽

Sar Imagery

Synthetic aperture radar (SAR) imagery has been used as a promising data source for monitoring maritime activities, and its application for oil and ship detection has been the focus of many previous research studies. Many object detection methods ranging from traditional to deep learning approaches have been proposed. However, majority of them are computationally intensive and have accuracy problems. The huge volume of the remote sensing data also brings a challenge for real time object detection. To mitigate this problem a high performance computing (HPC) method has been proposed to accelerate SAR imagery analysis, utilizing the GPU based computing methods. In this paper, we propose an enhanced GPU based deep learning method to detect ship from the SAR images. The You Only Look Once version 2 (YOLOv2) deep learning framework is proposed to model the architecture and training the model. YOLOv2 is a state-of-the-art real-time object detection system, which outperforms Faster Region-Based Convolutional Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) methods. Additionally, in order to reduce computational time with relatively competitive detection accuracy, we develop a new architecture with less number of layers called YOLOv2-reduced. In the experiment, we use two types of datasets: A SAR ship detection dataset (SSDD) dataset and a Diversified SAR Ship Detection Dataset (DSSDD). These two datasets were used for training and testing purposes. YOLOv2 test results showed an increase in accuracy of ship detection as well as a noticeable reduction in computational time compared to Faster R-CNN. From the experimental results, the proposed YOLOv2 architecture achieves an accuracy of 90.05% and 89.13% on the SSDD and DSSDD datasets respectively. The proposed YOLOv2-reduced architecture has a similarly competent detection performance as YOLOv2, but with less computational time on a NVIDIA TITAN X GPU. The experimental results shows that the deep learning can make a big leap forward in improving the performance of SAR image ship detection.

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Machine learning using synthetic images for detecting dust emissions on construction sites

Smart and Sustainable Built Environment ◽

10.1108/sasbe-04-2021-0066 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Ruoxin Xiong ◽

Pingbo Tang

Keyword(s):

Object Detection ◽

Real World ◽

State Of The Art ◽

Synthetic Image ◽

Single Shot ◽

Construction Sites ◽

Content Type ◽

Detection Algorithms ◽

Dust Detection ◽

Synthetic Images

PurposeAutomated dust monitoring in workplaces helps provide timely alerts to over-exposed workers and effective mitigation measures for proactive dust control. However, the cluttered nature of construction sites poses a practical challenge to obtain enough high-quality images in the real world. The study aims to establish a framework that overcomes the challenges of lacking sufficient imagery data (“data-hungry problem”) for training computer vision algorithms to monitor construction dust.Design/methodology/approachThis study develops a synthetic image generation method that incorporates virtual environments of construction dust for producing training samples. Three state-of-the-art object detection algorithms, including Faster-RCNN, you only look once (YOLO) and single shot detection (SSD), are trained using solely synthetic images. Finally, this research provides a comparative analysis of object detection algorithms for real-world dust monitoring regarding the accuracy and computational efficiency.FindingsThis study creates a construction dust emission (CDE) dataset consisting of 3,860 synthetic dust images as the training dataset and 1,015 real-world images as the testing dataset. The YOLO-v3 model achieves the best performance with a 0.93 F1 score and 31.44 fps among all three object detection models. The experimental results indicate that training dust detection algorithms with only synthetic images can achieve acceptable performance on real-world images.Originality/valueThis study provides insights into two questions: (1) how synthetic images could help train dust detection models to overcome data-hungry problems and (2) how well state-of-the-art deep learning algorithms can detect nonrigid construction dust.

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Comparison of Sneo-Based Neural Spike Detection Algorithms for Implantable Multi-Transistor Array Biosensors

Electronics ◽

10.3390/electronics10040410 ◽

2021 ◽

Vol 10 (4) ◽

pp. 410

Author(s):

Gerardo Saggese ◽

Mattia Tambaro ◽

Elia A. Vallicelli ◽

Antonio G. M. Strollo ◽

Stefano Vassanelli ◽

...

Keyword(s):

Firing Rate ◽

Energy Operator ◽

Neuronal Firing ◽

Capacitive Coupling ◽

Detection Accuracy ◽

Noise Power ◽

Spike Detection ◽

Low Snr ◽

Detection Algorithms ◽

Neural Spike

Real-time neural spike detection is an important step in understanding neurological activities and developing brain-silicon interfaces. Recent approaches exploit minimally invasive sensing techniques based on implanted complementary metal-oxide semiconductors (CMOS) multi transistors arrays (MTAs) that limit the damage of the neural tissue and provide high spatial resolution. Unfortunately, MTAs result in low signal-to-noise ratios due to the weak capacitive coupling between the nearby neurons and the sensor and the high noise power coming from the analog front-end. In this paper we investigate the performance achievable by using spike detection algorithms for MTAs, based on some variants of the smoothed non-linear energy operator (SNEO). We show that detection performance benefits from the correlation of the signals detected by the MTA pixels, but degrades when a high firing rate of neurons occurs. We present and compare different approaches and noise estimation techniques for the SNEO, aimed at increasing the detection accuracy at low SNR and making it less dependent on neurons firing rates. The algorithms are tested by using synthetic neural signals obtained with a modified version of NEUROCUBE generator. The proposed approaches outperform the SNEO, showing a more than 20% increase on averaged sensitivity at 0 dB and reduced dependence on the neuronal firing rate.

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Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images

Remote Sensing ◽

10.3390/rs12030458 ◽

2020 ◽

Vol 12 (3) ◽

pp. 458 ◽

Cited By ~ 7

Author(s):

Ugur Alganci ◽

Mehmet Soydas ◽

Elif Sertel

Keyword(s):

Deep Learning ◽

High Resolution ◽

Object Detection ◽

Satellite Images ◽

Training Data ◽

Object Localization ◽

Detection Accuracy ◽

Single Shot ◽

High Resolution Satellite Images ◽

Very High

Object detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution (VHR) satellite images. This article provides a comparative evaluation of the state-of-the-art convolutional neural network (CNN)-based object detection models, which are Faster R-CNN, Single Shot Multi-box Detector (SSD), and You Look Only Once-v3 (YOLO-v3), to cope with the limited number of labeled data and to automatically detect airplanes in VHR satellite images. Data augmentation with rotation, rescaling, and cropping was applied on the test images to artificially increase the number of training data from satellite images. Moreover, a non-maximum suppression algorithm (NMS) was introduced at the end of the SSD and YOLO-v3 flows to get rid of the multiple detection occurrences near each detected object in the overlapping areas. The trained networks were applied to five independent VHR test images that cover airports and their surroundings to evaluate their performance objectively. Accuracy assessment results of the test regions proved that Faster R-CNN architecture provided the highest accuracy according to the F1 scores, average precision (AP) metrics, and visual inspection of the results. The YOLO-v3 ranked as second, with a slightly lower performance but providing a balanced trade-off between accuracy and speed. The SSD provided the lowest detection performance, but it was better in object localization. The results were also evaluated in terms of the object size and detection accuracy manner, which proved that large- and medium-sized airplanes were detected with higher accuracy.

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SSD7-FFAM: A Real-Time Object Detection Network Friendly to Embedded Devices from Scratch

Applied Sciences ◽

10.3390/app11031096 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1096

Author(s):

Qing Li ◽

Yingcheng Lin ◽

Wei He

Keyword(s):

Object Detection ◽

Real Time ◽

Large Scale ◽

Feature Fusion ◽

Contextual Information ◽

Attention Mechanism ◽

Detection Accuracy ◽

Single Shot ◽

Feature Maps ◽

Embedded Devices

The high requirements for computing and memory are the biggest challenges in deploying existing object detection networks to embedded devices. Living lightweight object detectors directly use lightweight neural network architectures such as MobileNet or ShuffleNet pre-trained on large-scale classification datasets, which results in poor network structure flexibility and is not suitable for some specific scenarios. In this paper, we propose a lightweight object detection network Single-Shot MultiBox Detector (SSD)7-Feature Fusion and Attention Mechanism (FFAM), which saves storage space and reduces the amount of calculation by reducing the number of convolutional layers. We offer a novel Feature Fusion and Attention Mechanism (FFAM) method to improve detection accuracy. Firstly, the FFAM method fuses high-level semantic information-rich feature maps with low-level feature maps to improve small objects’ detection accuracy. The lightweight attention mechanism cascaded by channels and spatial attention modules is employed to enhance the target’s contextual information and guide the network to focus on its easy-to-recognize features. The SSD7-FFAM achieves 83.7% mean Average Precision (mAP), 1.66 MB parameters, and 0.033 s average running time on the NWPU VHR-10 dataset. The results indicate that the proposed SSD7-FFAM is more suitable for deployment to embedded devices for real-time object detection.

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Lightweight Detection Network Based on Sub-Pixel Convolution and Objectness-Aware Structure for UAV Images

Sensors ◽

10.3390/s21165656 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5656

Author(s):

Xuanye Li ◽

Hongguang Li ◽

Yalong Jiang ◽

Meng Wang

Keyword(s):

Object Detection ◽

Computational Cost ◽

Frame Rate ◽

Detection Methods ◽

Detection Accuracy ◽

Small Object ◽

Mobile Platforms ◽

Efficient Detection ◽

Uav Images ◽

Uav Image

Unmanned Aerial Vehicles (UAVs) can serve as an ideal mobile platform in various situations. Real-time object detection with on-board apparatus provides drones with increased flexibility as well as a higher intelligence level. In order to achieve good detection results in UAV images with complex ground scenes, small object size and high object density, most of the previous work introduced models with higher computational burdens, making deployment on mobile platforms more difficult.This paper puts forward a lightweight object detection framework. Besides being anchor-free, the framework is based on a lightweight backbone and a simultaneous up-sampling and detection module to form a more efficient detection architecture. Meanwhile, we add an objectness branch to assist the multi-class center point prediction, which notably improves the detection accuracy and only takes up very little computing resources. The results of the experiment indicate that the computational cost of this paper is 92.78% lower than the CenterNet with ResNet18 backbone, and the mAP is 2.8 points higher on the Visdrone-2018-VID dataset. A frame rate of about 220 FPS is achieved. Additionally, we perform ablation experiments to check on the validity of each part, and the method we propose is compared with other representative lightweight object detection methods on UAV image datasets.

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