Developing a microscopic image dataset in support of intelligent phytoplankton detection using deep learning

Abstract Phytoplankton plays an important role in marine ecological environment and aquaculture. However, the recognition and detection of phytoplankton rely on manual operations. As the foundation of achieving intelligence and releasing human labour, a phytoplankton microscopic image dataset PMID2019 for phytoplankton automated detection is presented. The PMID2019 dataset contains 10 819 phytoplankton microscopic images of 24 different categories. We leverage microscopes to collect images of phytoplankton in the laboratory environment. Each object in the images is manually labelled with a bounding box and category of ground-truth. In addition, living cells move quickly making it difficult to capture images of them. In order to generalize the dataset for in situ applications, we further utilize Cycle-GAN to achieve the domain migration between dead and living cell samples. We built a synthetic dataset to generate the corresponding living cell samples from the original dead ones. The PMID2019 dataset will not only benefit the development of phytoplankton microscopic vision technology in the future, but also can be widely used to assess the performance of the state-of-the-art object detection algorithms for phytoplankton recognition. Finally, we illustrate the performances of some state-of-the-art object detection algorithms, which may provide new ideas for monitoring marine ecosystems.

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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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A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image

Applied Sciences ◽

10.3390/app112311241 ◽

2021 ◽

Vol 11 (23) ◽

pp. 11241

Author(s):

Ling Li ◽

Fei Xue ◽

Dong Liang ◽

Xiaofei Chen

Keyword(s):

Computer Vision ◽

Object Detection ◽

State Of The Art ◽

Terahertz Imaging ◽

Public Security ◽

Counter Terrorism ◽

Detection Algorithms ◽

Public Dataset ◽

The One ◽

Objects Detection

Concealed objects detection in terahertz imaging is an urgent need for public security and counter-terrorism. So far, there is no public terahertz imaging dataset for the evaluation of objects detection algorithms. This paper provides a public dataset for evaluating multi-object detection algorithms in active terahertz imaging. Due to high sample similarity and poor imaging quality, object detection on this dataset is much more difficult than on those commonly used public object detection datasets in the computer vision field. Since the traditional hard example mining approach is designed based on the two-stage detector and cannot be directly applied to the one-stage detector, this paper designs an image-based Hard Example Mining (HEM) scheme based on RetinaNet. Several state-of-the-art detectors, including YOLOv3, YOLOv4, FRCN-OHEM, and RetinaNet, are evaluated on this dataset. Experimental results show that the RetinaNet achieves the best mAP and HEM further enhances the performance of the model. The parameters affecting the detection metrics of individual images are summarized and analyzed in the experiments.

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Deep-Learning-Based Road Crack Detection Frameworks for Dashcam-captured Images under Different Illumination Conditions

10.21203/rs.3.rs-685762/v1 ◽

2021 ◽

Author(s):

Da-Ren Chen ◽

Wei-Min Chiu

Keyword(s):

Object Detection ◽

Large Scale ◽

Crack Detection ◽

State Of The Art ◽

Gaussian Mixture Models ◽

Gaussian Mixture ◽

Machine Learning Techniques ◽

Detection Accuracy ◽

The Road ◽

Art Object

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.

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PI-RCNN: An Efficient Multi-Sensor 3D Object Detector with Point-Based Attentive Cont-Conv Fusion Module

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6933 ◽

2020 ◽

Vol 34 (07) ◽

pp. 12460-12467

Author(s):

Liang Xie ◽

Chao Xiang ◽

Zhengxu Yu ◽

Guodong Xu ◽

Zheng Yang ◽

...

Keyword(s):

Object Detection ◽

State Of The Art ◽

Point Clouds ◽

Semantic Features ◽

Feature Maps ◽

3D Object ◽

Detection Algorithms ◽

Full Resolution ◽

Fusion Methods ◽

3D Object Detection

LIDAR point clouds and RGB-images are both extremely essential for 3D object detection. So many state-of-the-art 3D detection algorithms dedicate in fusing these two types of data effectively. However, their fusion methods based on Bird's Eye View (BEV) or voxel format are not accurate. In this paper, we propose a novel fusion approach named Point-based Attentive Cont-conv Fusion(PACF) module, which fuses multi-sensor features directly on 3D points. Except for continuous convolution, we additionally add a Point-Pooling and an Attentive Aggregation to make the fused features more expressive. Moreover, based on the PACF module, we propose a 3D multi-sensor multi-task network called Pointcloud-Image RCNN(PI-RCNN as brief), which handles the image segmentation and 3D object detection tasks. PI-RCNN employs a segmentation sub-network to extract full-resolution semantic feature maps from images and then fuses the multi-sensor features via powerful PACF module. Beneficial from the effectiveness of the PACF module and the expressive semantic features from the segmentation module, PI-RCNN can improve much in 3D object detection. We demonstrate the effectiveness of the PACF module and PI-RCNN on the KITTI 3D Detection benchmark, and our method can achieve state-of-the-art on the metric of 3D AP.

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IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery

Remote Sensing ◽

10.3390/rs11030286 ◽

2019 ◽

Vol 11 (3) ◽

pp. 286 ◽

Cited By ~ 24

Author(s):

Jiangqiao Yan ◽

Hongqi Wang ◽

Menglong Yan ◽

Wenhui Diao ◽

Xian Sun ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Object Detection ◽

Convolutional Neural Network ◽

State Of The Art ◽

Ground Truth ◽

Detection Performance ◽

Candidate Region ◽

Detection Accuracy ◽

Remote Sensing Images

Recently, methods based on Faster region-based convolutional neural network (R-CNN)have been popular in multi-class object detection in remote sensing images due to their outstandingdetection performance. The methods generally propose candidate region of interests (ROIs) througha region propose network (RPN), and the regions with high enough intersection-over-union (IoU)values against ground truth are treated as positive samples for training. In this paper, we find thatthe detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially,detection performance of small objects is poor when choosing a normal higher threshold, while alower threshold will result in poor location accuracy caused by a large quantity of false positives.To address the above issues, we propose a novel IoU-Adaptive Deformable R-CNN framework formulti-class object detection. Specially, by analyzing the different roles that IoU can play in differentparts of the network, we propose an IoU-guided detection framework to reduce the loss of small objectinformation during training. Besides, the IoU-based weighted loss is designed, which can learn theIoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspectratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves theprecision of the results. Extensive experiments validate the effectiveness of our approach and weachieve state-of-the-art detection performance on the DOTA dataset.

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TasselNetV2+: A Fast Implementation for High-Throughput Plant Counting From High-Resolution RGB Imagery

Frontiers in Plant Science ◽

10.3389/fpls.2020.541960 ◽

2020 ◽

Vol 11 ◽

Author(s):

Hao Lu ◽

Zhiguo Cao

Keyword(s):

High Resolution ◽

Object Detection ◽

High Throughput ◽

Graphics Processing Units ◽

State Of The Art ◽

Image Resolution ◽

Plant Phenotyping ◽

Art Object ◽

Bounding Boxes ◽

Computational Bottleneck

Plant counting runs through almost every stage of agricultural production from seed breeding, germination, cultivation, fertilization, pollination to yield estimation, and harvesting. With the prevalence of digital cameras, graphics processing units and deep learning-based computer vision technology, plant counting has gradually shifted from traditional manual observation to vision-based automated solutions. One of popular solutions is a state-of-the-art object detection technique called Faster R-CNN where plant counts can be estimated from the number of bounding boxes detected. It has become a standard configuration for many plant counting systems in plant phenotyping. Faster R-CNN, however, is expensive in computation, particularly when dealing with high-resolution images. Unfortunately high-resolution imagery is frequently used in modern plant phenotyping platforms such as unmanned aerial vehicles, engendering inefficient image analysis. Such inefficiency largely limits the throughput of a phenotyping system. The goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery. In contrast to conventional object detection, we encourage another promising paradigm termed object counting where plant counts are directly regressed from images, without detecting bounding boxes. In this work, by profiling the computational bottleneck, we implement a fast version of a state-of-the-art plant counting model TasselNetV2 with several minor yet effective modifications. We also provide insights why these modifications make sense. This fast version, TasselNetV2+, runs an order of magnitude faster than TasselNetV2, achieving around 30 fps on image resolution of 1980 × 1080, while it still retains the same level of counting accuracy. We validate its effectiveness on three plant counting tasks, including wheat ears counting, maize tassels counting, and sorghum heads counting. To encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plus.

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Ground Truth Annotated Femoral X-Ray Image Dataset and Object Detection Based Method for Fracture Types Classification

IEEE Access ◽

10.1109/access.2020.3029039 ◽

2020 ◽

Vol 8 ◽

pp. 189436-189444 ◽

Cited By ~ 2

Author(s):

Yubin Qi ◽

Jing Zhao ◽

Yongan Shi ◽

Guilai Zuo ◽

Haonan Zhang ◽

...

Keyword(s):

Object Detection ◽

Ground Truth ◽

X Ray ◽

Image Dataset ◽

Fracture Types

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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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A Fast Orientation Invariant Detector Based on the One-stage Method

MATEC Web of Conferences ◽

10.1051/matecconf/201823204036 ◽

2018 ◽

Vol 232 ◽

pp. 04036

Author(s):

Jun Yin ◽

Huadong Pan ◽

Hui Su ◽

Zhonggeng Liu ◽

Zhirong Peng

Keyword(s):

Object Detection ◽

Loss Function ◽

High Efficiency ◽

Detection Method ◽

State Of The Art ◽

Orientation Angle ◽

Detection Methods ◽

Detection Algorithms ◽

Bounding Boxes ◽

The One

We propose an object detection method that predicts the orientation bounding boxes (OBB) to estimate objects locations, scales and orientations based on YOLO (You Only Look Once), which is one of the top detection algorithms performing well both in accuracy and speed. Horizontal bounding boxes(HBB), which are not robust to orientation variances, are used in the existing object detection methods to detect targets. The proposed orientation invariant YOLO (OIYOLO) detector can effectively deal with the bird’s eye viewpoint images where the orientation angles of the objects are arbitrary. In order to estimate the rotated angle of objects, we design a new angle loss function. Therefore, the training of OIYOLO forces the network to learn the annotated orientation angle of objects, making OIYOLO orientation invariances. The proposed approach that predicts OBB can be applied in other detection frameworks. In additional, to evaluate the proposed OIYOLO detector, we create an UAV-DAHUA datasets that annotated with objects locations, scales and orientation angles accurately. Extensive experiments conducted on UAV-DAHUA and DOTA datasets demonstrate that OIYOLO achieves state-of-the-art detection performance with high efficiency comparing with the baseline YOLO algorithms.

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AUTOMATIC OBJECT DETECTION AND SEGMENTATION OF THE HISTOCYTOLOGY IMAGES USING RESHAPABLE AGENTS

Image Analysis & Stereology ◽

10.5566/ias.v32.p89-99 ◽

2013 ◽

Vol 32 (2) ◽

pp. 89 ◽

Cited By ~ 7

Author(s):

Mehdi Alilou ◽

Vassili Kovalev

Keyword(s):

Object Detection ◽

Cost Function ◽

State Of The Art ◽

Automatic Detection ◽

Target Cells ◽

Segmentation Method ◽

Microscopy Image ◽

Image Dataset ◽

The Cost ◽

Prior Shape

The aim of this study is to suggest a method for automatic detection and segmentation of the target objects in the microscopic histology/cytology images. The detection is carried out by rectangular shapes then segmentation process starts utilizing flexible agents which are able to move and change their shapes according to a cost function. The agents are rectangular at the beginning then they gradually fit to the corresponding objects using a stochastic reshaping algorithm. The iterative reshaping process is controlled by a cost function and it is resulted in a finer segmentation of the target objects. The cost functional of the proposed method comprised of three terms including the prior shape, regional texture and gradient information. The experiments were carried out using a publicly available microscopy image dataset which contains 510 manually-labeled target cells. The segmentation performance of the proposed method is compared with another state of the art segmentation method. The results demonstrate satisfactory detection and segmentation performance of the proposed method.

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