Multiscale and Direction Target Detecting in Remote Sensing Images via Modified YOLO-v4

Zakria, Zakria; Deng, Jianhua; Kumar, Rajesh; Khokhar, Muhammad Saddam; Cai, Jingye; Kumar, Jay

doi:10.1109/jstars.2022.3140776

Cited by 76 publications

(33 citation statements)

References 37 publications

(55 reference statements)

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“…Thus, the performance of the trained detection algorithm has been further improved. Furthermore, the scaled-YOLOv4 [44] has been applied to detect objects in many application areas including remote sensing [49], natural scene images [26], and many others [7,38,46]. To the best of our knowledge, this study is the first successful attempt at applying scaled-YOLOv4 to detection of nuclei in WSIs.…”

Section: Mitnet-det: Nuclei Detection In Whole Slide Images Of Breast...mentioning

confidence: 99%

MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue

Çayır¹,

Solmaz²,

Kusetoğulları

et al. 2022

Neural Comput & Applic

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Mitosis assessment of breast cancer has a strong prognostic importance and is visually evaluated by pathologists. The inter, and intra-observer variability of this assessment is high. In this paper, a two-stage deep learning approach, named MITNET, has been applied to automatically detect nucleus and classify mitoses in whole slide images (WSI) of breast cancer. Moreover, this paper introduces two new datasets. The first dataset is used to detect the nucleus in the WSIs, which contains 139,124 annotated nuclei in 1749 patches extracted from 115 WSIs of breast cancer tissue, and the second dataset consists of 4908 mitotic cells and 4908 non-mitotic cells image samples extracted from 214 WSIs which is used for mitosis classification. The created datasets are used to train the MITNET network, which consists of two deep learning architectures, called MITNET-det and MITNET-rec, respectively, to isolate nuclei cells and identify the mitoses in WSIs. In MITNET-det architecture, to extract features from nucleus images and fuse them, CSPDarknet and Path Aggregation Network (PANet) are used, respectively, and then, a detection strategy using You Look Only Once (scaled-YOLOv4) is employed to detect nucleus at three different scales. In the classification part, the detected isolated nucleus images are passed through proposed MITNET-rec deep learning architecture, to identify the mitosis in the WSIs. Various deep learning classifiers and the proposed classifier are trained with a publicly available mitosis datasets (MIDOG and ATYPIA) and then, validated over our created dataset. The results verify that deep learning-based classifiers trained on MIDOG and ATYPIA have difficulties to recognize mitosis on our dataset which shows that the created mitosis dataset has unique features and characteristics. Besides this, the proposed classifier outperforms the state-of-the-art classifiers significantly and achieves a $$68.7\%$$ 68.7 % F1-score and $$49.0\%$$ 49.0 % F1-score on the MIDOG and the created mitosis datasets, respectively. Moreover, the experimental results reveal that the overall proposed MITNET framework detects the nucleus in WSIs with high detection rates and recognizes the mitotic cells in WSI with high F1-score which leads to the improvement of the accuracy of pathologists’ decision.

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Section: Mitnet-det: Nuclei Detection In Whole Slide Images Of Breast...mentioning

confidence: 99%

MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue

Çayır¹,

Solmaz²,

Kusetoğulları

et al. 2022

Neural Comput & Applic

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“…But still, challenges affect the performance of the object detector to meet with real-time performance like human beings. Tremendous research works have been done in object detection to handle the challenges in different application areas as shown in the Table 4 [ 102 , 107 , 123 , 164 , 168 , 188 , 194 , 200 , 208 , 213 , 215 ]. For example, Li.…”

Section: Discussionmentioning

confidence: 99%

“…Wu et al have developed the target detection method in which DarkNet53 is improved based on YOLOV3 [ 194 ]. Due to the best performance of the YOLO architecture, Zakria et al have also presented the approach with some modifications in YOLO-v4 for the detection of multiscale and direction targets from remote sensing images [ 208 ]. In this, they proposed the classification setting of the NMS threshold so that the method’s accuracy is increased without affecting the speed.…”

Section: Applications Of Object Detectionmentioning

confidence: 99%

Tools, techniques, datasets and application areas for object detection in an image: a review

Kaur

Singh

2022

Multimed Tools Appl

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Object detection is one of the most fundamental and challenging tasks to locate objects in images and videos. Over the past, it has gained much attention to do more research on computer vision tasks such as object classification, counting of objects, and object monitoring. This study provides a detailed literature review focusing on object detection and discusses the object detection techniques. A systematic review has been followed to summarize the current research work’s findings and discuss seven research questions related to object detection. Our contribution to the current research work is (i) analysis of traditional, two-stage, one-stage object detection techniques, (ii) Dataset preparation and available standard dataset, (iii) Annotation tools, and (iv) performance evaluation metrics. In addition, a comparative analysis has been performed and analyzed that the proposed techniques are different in their architecture, optimization function, and training strategies. With the remarkable success of deep neural networks in object detection, the performance of the detectors has improved. Various research challenges and future directions for object detection also has been discussed in this research paper.

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“…SPP can integrate multi-scale perceptual field information and extract top and bottom features without any significant decrease in the network processing speed. PAN network structure enhances the feature hierarchy with precisely located signals using a bottom-up path enhancement method, shortens the information path between the bottom and topmost layers, and avoids the information loss problem, while the information obtained from the feature map after stitching contains both bottom and semantic features, realizing the two-way fusion of feature information from deep to shallow and from shallow to deep layers [50,51].…”

Section: Yolov4mentioning

confidence: 99%

Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery

Wang

Huang

2022

Remote Sensing

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With the rapid development of remote sensing with small, lightweight unmanned aerial vehicles (UAV), efficient and accurate crop spike counting, and yield estimation methods based on deep learning (DL) methods have begun to emerge, greatly reducing labor costs and enabling fast and accurate counting of sorghum spikes. However, there has not been a systematic, comprehensive evaluation of their applicability in cereal crop spike identification in UAV images, especially in sorghum head counting. To this end, this paper conducts a comparative study of the performance of three common DL algorithms, EfficientDet, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv4), for sorghum head detection based on lightweight UAV remote sensing data. The paper explores the effects of overlap ratio, confidence, and intersection over union (IoU) parameters, using the evaluation metrics of precision P, recall R, average precision AP, F1 score, computational efficiency, and the number of detected positive/negative samples (Objects detected consistent/inconsistent with real samples). The experiment results show the following. (1) The detection results of the three methods under dense coverage conditions were better than those under medium and sparse conditions. YOLOv4 had the most accurate detection under different coverage conditions; on the contrary, EfficientDet was the worst. While SSD obtained better detection results under dense conditions, the number of over-detections was larger. (2) It was concluded that although EfficientDet had a good positive sample detection rate, it detected the fewest samples, had the smallest R and F1, and its actual precision was poor, while its training time, although medium, had the lowest detection efficiency, and the detection time per image was 2.82-times that of SSD. SSD had medium values for P, AP, and the number of detected samples, but had the highest training and detection efficiency. YOLOv4 detected the largest number of positive samples, and its values for R, AP, and F1 were the highest among the three methods. Although the training time was the slowest, the detection efficiency was better than EfficientDet. (3) With an increase in the overlap ratios, both positive and negative samples tended to increase, and when the threshold value was 0.3, all three methods had better detection results. With an increase in the confidence value, the number of positive and negative samples significantly decreased, and when the threshold value was 0.3, it balanced the numbers for sample detection and detection accuracy. An increase in IoU was accompanied by a gradual decrease in the number of positive samples and a gradual increase in the number of negative samples. When the threshold value was 0.3, better detection was achieved. The research findings can provide a methodological basis for accurately detecting and counting sorghum heads using UAV.

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Multiscale and Direction Target Detecting in Remote Sensing Images via Modified YOLO-v4

Cited by 76 publications

References 37 publications

MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue

MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue

Tools, techniques, datasets and application areas for object detection in an image: a review

Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery

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