scholarly journals A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 372
Author(s):  
Jian-Wen Chen ◽  
Wan-Ju Lin ◽  
Hui-Jun Cheng ◽  
Che-Lun Hung ◽  
Chun-Yuan Lin ◽  
...  
Keyword(s):  
Object Detection ◽  
Detection System ◽  
Scale Insect ◽  
Agricultural Products ◽  
Detection Methods ◽  
Single Shot ◽  
Agricultural Field ◽  
Crop Losses ◽  
Computational Performance ◽  
Pest Detection

Taiwan’s economy mainly relies on the export of agricultural products. If even the suspicion of a pest is found in the crop products after they are exported, not only are the agricultural products returned but the whole batch of crops is destroyed, resulting in extreme crop losses. The species of mealybugs, Coccidae, and Diaspididae, which are the primary pests of the scale insect in Taiwan, can not only lead to serious damage to the plants but also severely affect agricultural production. Hence, to recognize the scale pests is an important task in Taiwan’s agricultural field. In this study, we propose an AI-based pest detection system for solving the specific issue of detection of scale pests based on pictures. Deep-learning-based object detection models, such as faster region-based convolutional networks (Faster R-CNNs), single-shot multibox detectors (SSDs), and You Only Look Once v4 (YOLO v4), are employed to detect and localize scale pests in the picture. The experimental results show that YOLO v4 achieved the highest classification accuracy among the algorithms, with 100% in mealybugs, 89% in Coccidae, and 97% in Diaspididae. Meanwhile, the computational performance of YOLO v4 has indicated that it is suitable for real-time application. Moreover, the inference results of the YOLO v4 model further help the end user. A mobile application using the trained scale pest recognition model has been developed to facilitate pest identification in farms, which is helpful in applying appropriate pesticides to reduce crop losses.

scholarly journals Object detection in real time based on improved single shot multi-box detector algorithm

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Ashwani Kumar ◽  
Zuopeng Justin Zhang ◽  
Hongbo Lyu
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Single Layer ◽  
Single Shot ◽  
Convolutional Network ◽  
Detection Techniques ◽  
Computational Performance ◽  
Speed Up

Abstract In today’s scenario, the fastest algorithm which uses a single layer of convolutional network to detect the objects from the image is single shot multi-box detector (SSD) algorithm. This paper studies object detection techniques to detect objects in real time on any device running the proposed model in any environment. In this paper, we have increased the classification accuracy of detecting objects by improving the SSD algorithm while keeping the speed constant. These improvements have been done in their convolutional layers, by using depth-wise separable convolution along with spatial separable convolutions generally called multilayer convolutional neural networks. The proposed method uses these multilayer convolutional neural networks to develop a system model which consists of multilayers to classify the given objects into any of the defined classes. The schemes then use multiple images and detect the objects from these images, labeling them with their respective class label. To speed up the computational performance, the proposed algorithm is applied along with the multilayer convolutional neural network which uses a larger number of default boxes and results in more accurate detection. The accuracy in detecting the objects is checked by different parameters such as loss function, frames per second (FPS), mean average precision (mAP), and aspect ratio. Experimental results confirm that our proposed improved SSD algorithm has high accuracy.

scholarly journals Ship Detection Based on YOLOv2 for SAR Imagery

2019 ◽  
Vol 11 (7) ◽  
pp. 786 ◽  
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.

scholarly journals Lightweight Feature Enhancement Network for Single-Shot Object Detection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1066
Author(s):  
Peng Jia ◽  
Fuxiang Liu
Keyword(s):  
Receptive Field ◽  
Object Detection ◽  
Real Time ◽  
Detection System ◽  
Field Enhancement ◽  
Detection Performance ◽  
Feature Representation ◽  
Data Sets ◽  
Single Shot ◽  
Enhancement Method

At present, the one-stage detector based on the lightweight model can achieve real-time speed, but the detection performance is challenging. To enhance the discriminability and robustness of the model extraction features and improve the detector’s detection performance for small objects, we propose two modules in this work. First, we propose a receptive field enhancement method, referred to as adaptive receptive field fusion (ARFF). It enhances the model’s feature representation ability by adaptively learning the fusion weights of different receptive field branches in the receptive field module. Then, we propose an enhanced up-sampling (EU) module to reduce the information loss caused by up-sampling on the feature map. Finally, we assemble ARFF and EU modules on top of YOLO v3 to build a real-time, high-precision and lightweight object detection system referred to as the ARFF-EU network. We achieve a state-of-the-art speed and accuracy trade-off on both the Pascal VOC and MS COCO data sets, reporting 83.6% AP at 37.5 FPS and 42.5% AP at 33.7 FPS, respectively. The experimental results show that our proposed ARFF and EU modules improve the detection performance of the ARFF-EU network and achieve the development of advanced, very deep detectors while maintaining real-time speed.

A brief review and challenges of object detection in optical remote sensing imagery

2020 ◽  
Vol 16 (3) ◽  
pp. 227-243
Author(s):  
Shahid Karim ◽  
Ye Zhang ◽  
Shoulin Yin ◽  
Irfana Bibi ◽  
Ali Anwar Brohi
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
Detection Methods ◽  
Optical Remote Sensing ◽  
Single Shot ◽  
Processing Efficiency ◽  
Regression Problem ◽  
Detection Algorithms ◽  
Sensing Applications

Traditional object detection algorithms and strategies are difficult to meet the requirements of data processing efficiency, performance, speed and intelligence in object detection. Through the study and imitation of the cognitive ability of the brain, deep learning can analyze and process the data features. It has a strong ability of visualization and becomes the mainstream algorithm of current object detection applications. Firstly, we have discussed the developments of traditional object detection methods. Secondly, the frameworks of object detection (e.g. Region-based CNN (R-CNN), Spatial Pyramid Pooling Network (SPP-NET), Fast-RCNN and Faster-RCNN) which combine region proposals and convolutional neural networks (CNNs) are briefly characterized for optical remote sensing applications. You only look once (YOLO) algorithm is the representative of the object detection frameworks (e.g. YOLO and Single Shot MultiBox Detector (SSD)) which transforms the object detection into a regression problem. The limitations of remote sensing images and object detectors have been highlighted and discussed. The feasibility and limitations of these approaches will lead researchers to prudently select appropriate image enhancements. Finally, the problems of object detection algorithms in deep learning are summarized and the future recommendations are also conferred.

scholarly journals Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset

2020 ◽  
Vol 11 (3) ◽  
pp. 144
Author(s):  
Yonatan Adiwinata ◽  
Akane Sasaoka ◽  
I Putu Agung Bayupati ◽  
Oka Sudana
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Fish Species ◽  
Species Recognition ◽  
Species Conservation ◽  
Detection Methods ◽  
Single Shot ◽  
Natural Ecosystems ◽  
Efficient Technology

Fish species conservation had a big impact on the natural ecosystems balanced. The existence of efficient technology in identifying fish species could help fish conservation. The most recent research related to was a classification of fish species using the Deep Learning method. Most of the deep learning methods used were Convolutional Layer or Convolutional Neural Network (CNN). This research experimented with using object detection method based on deep learning like Faster R-CNN, which possible to recognize the species of fish inside of the image without more image preprocessing. This research aimed to know the performance of the Faster R-CNN method against other object detection methods like SSD in fish species detection. The fish dataset used in the research reference was QUT FISH Dataset. The accuracy of the Faster R-CNN reached 80.4%, far above the accuracy of the Single Shot Detector (SSD) Model with an accuracy of 49.2%.  

scholarly journals A Single Shot Framework with Multi-Scale Feature Fusion for Geospatial Object Detection

2019 ◽  
Vol 11 (5) ◽  
pp. 594 ◽  
Author(s):  
Shuo Zhuang ◽  
Ping Wang ◽  
Boran Jiang ◽  
Gang Wang ◽  
Cong Wang
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Large Scale ◽  
Feature Fusion ◽  
Aerial Images ◽  
Detection Methods ◽  
Single Shot ◽  
Feature Maps ◽  
Scale Feature ◽  
Multi Scale

With the rapid advances in remote-sensing technologies and the larger number of satellite images, fast and effective object detection plays an important role in understanding and analyzing image information, which could be further applied to civilian and military fields. Recently object detection methods with region-based convolutional neural network have shown excellent performance. However, these two-stage methods contain region proposal generation and object detection procedures, resulting in low computation speed. Because of the expensive manual costs, the quantity of well-annotated aerial images is scarce, which also limits the progress of geospatial object detection in remote sensing. In this paper, on the one hand, we construct and release a large-scale remote-sensing dataset for geospatial object detection (RSD-GOD) that consists of 5 different categories with 18,187 annotated images and 40,990 instances. On the other hand, we design a single shot detection framework with multi-scale feature fusion. The feature maps from different layers are fused together through the up-sampling and concatenation blocks to predict the detection results. High-level features with semantic information and low-level features with fine details are fully explored for detection tasks, especially for small objects. Meanwhile, a soft non-maximum suppression strategy is put into practice to select the final detection results. Extensive experiments have been conducted on two datasets to evaluate the designed network. Results show that the proposed approach achieves a good detection performance and obtains the mean average precision value of 89.0% on a newly constructed RSD-GOD dataset and 83.8% on the Northwestern Polytechnical University very high spatial resolution-10 (NWPU VHR-10) dataset at 18 frames per second (FPS) on a NVIDIA GTX-1080Ti GPU.

scholarly journals Multi-View Object Detection Based on Deep Learning

2018 ◽  
Vol 8 (9) ◽  
pp. 1423 ◽  
Author(s):  
Cong Tang ◽  
Yongshun Ling ◽  
Xing Yang ◽  
Wei Jin ◽  
Chao Zheng
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Regression Models ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Single Shot ◽  
Small Object ◽  
Object Retrieval ◽  
Detection Approach ◽  
Small Object Detection

A multi-view object detection approach based on deep learning is proposed in this paper. Classical object detection methods based on regression models are introduced, and the reasons for their weak ability to detect small objects are analyzed. To improve the performance of these methods, a multi-view object detection approach is proposed, and the model structure and working principles of this approach are explained. Additionally, the object retrieval ability and object detection accuracy of both the multi-view methods and the corresponding classical methods are evaluated and compared based on a test on a small object dataset. The experimental results show that in terms of object retrieval capability, Multi-view YOLO (You Only Look Once: Unified, Real-Time Object Detection), Multi-view YOLOv2 (based on an updated version of YOLO), and Multi-view SSD (Single Shot Multibox Detector) achieve AF (average F-measure) scores that are higher than those of their classical counterparts by 0.177, 0.06, and 0.169, respectively. Moreover, in terms of the detection accuracy, when difficult objects are not included, the mAP (mean average precision) scores of the multi-view methods are higher than those of the classical methods by 14.3%, 7.4%, and 13.1%, respectively. Thus, the validity of the approach proposed in this paper has been verified. In addition, compared with state-of-the-art methods based on region proposals, multi-view detection methods are faster while achieving mAPs that are approximately the same in small object detection.

scholarly journals The Implementation Of Target Recognation to Determine Enemy Coordinates Using Unmanned Aerial Vehicle (UAV) GALAK-24 Aircraft Cameras With Object Detection Method

2020 ◽  
Vol 2 (Oktober) ◽  
pp. 20-28
Author(s):  
Mehmek Ali Akza Arsyad ◽  
Isa Mahfudi ◽  
Bambang Purwanto
Keyword(s):  
Decision Making ◽  
Object Detection ◽  
Unmanned Aerial Vehicle ◽  
Programming Language ◽  
Detection Method ◽  
Detection System ◽  
Detection Methods ◽  
Aerial Vehicle ◽  
The Military ◽  
Attacks And Defenses

Abstract – In this era of increasingly advance, camera technology to make it easier for the military to carry out attacks and defenses to destroy embattled opponents, for that is requires camera technology that can detect objects at once with the coordinates or position of the object cleary, so as to help troops to maximize attacks and maneuvers in war. This research is expected to develop GALAK-24 aitcraft equipped with enemy detection cameras and at the same  time determine the position of enemy coorninates in real time in helping intelligence on the bettlefield, thus facilitating decision-making in warfare. The detection system uses object Detection methods to detect objects that are on the surface of the land crossed by the aircraft. The workings of this detection camera is to use the phython programming language thats is connected to the PC and connected also to the camera, when the aircraft makes a flight across enemy territory then the camera will capture the entire enemy territory so that there are vehicle object recorded as well, the target will be reported to calculate the enemy’s strength and enemy position. For security prosedures the aircraft will be flown at on altitude of 500 (m) to avoid enemy personnel fire and also reduce noise so as not to be heard by the enemy reporting the condition of enemy territory, enemy forces at the same time and sent to the operator.

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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