Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection

Xu, Danqing; Wu, Yiquan

doi:10.3390/s20154276

Cited by 94 publications

(65 citation statements)

References 51 publications

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“…Before training, we used K-means cluster mothed to define the sizes of the anchor boxes. We set k=9, after experiment, the result showed 9 different size of anchor boxes, they were (10,25), (12,44), (12,38), (14,23), (16,32), (18,55), (19,22), (24,26), (44, 35), while the pixel size of the image was fixed to 416×416.…”

Section: Model Structurementioning

confidence: 99%

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Weak and Occluded Vehicle Detection in Complex Infrared Environment Based on Improved YOLOv4

Zhang

et al. 2021

IEEE Access

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Infrared small target detection is still a challenge in the field of object detection. At present, although there are many related research achievements, it surely needs further improvement. This paper introduced a new application of severely occluded vehicle detection in the complex wild background of weak infrared camera aerial images, in which more than 50% area of the vehicles are occluded. We used YOLOv4 as the detection model. By applying secondary transfer learning from visible dataset to infrared dataset, the model could gain a good average precision (AP). Firstly, we trained the model in the UCAS_AOD visible dataset, then, we transferred it to the VIVID visible dataset, finally we transferred the model to the VIVID infrared dataset for a second training. Meanwhile, added the hard negative example mining block to the YOLOv4 model, which could depress the disturbance of complex background thus further decrease the false detecting rate. Through experiments the average precision improved from90.34% to 91.92%, the F1 score improved from 87.5% to 87.98%, which demonstrated that the proposed algorithm generated satisfactory and competitive vehicle detection results. INDEX TERMS Infrared aerial image, occlusion, vehicle detection, hard negative example mining, YOLOv4.

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Section: Model Structurementioning

confidence: 99%

“…Small scale object detection is a hot and challenging task in the field of object detection. For small scale object, the main methods are feature fusing [14] and multi scale fusing [15].…”

Section: Introductionmentioning

confidence: 99%

Weak and Occluded Vehicle Detection in Complex Infrared Environment Based on Improved YOLOv4

Zhang

et al. 2021

IEEE Access

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“…Experimental results validated the YOLOv3 model, which performed well on insulator detection. To detect remote sensing targets from complex backgrounds, Xu et al [45] proposed a multiscale method based on improved YOLOv3 for remote sensing target detection. Experimental results demonstrated that the mean average precision of the proposed method was 10% higher than that of the original YOLOv3.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference

Liu

et al. 2021

Electronics

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Automatic inspection of insulators from high-voltage transmission lines is of paramount importance to the safety and reliable operation of the power grid. Due to different size insulators and the complex background of aerial images, it is a difficult task to recognize insulators in aerial views. Most of the traditional image processing methods and machine learning methods cannot achieve sufficient performance for insulator detection when diverse background interference is present. In this study, a deep learning method—based on You Only Look Once (YOLO)—will be proposed, capable of detecting insulators from aerial images with complex backgrounds. Firstly, aerial images with common aerial scenes were collected by Unmanned Aerial Vehicle (UAV), and a novel insulator dataset was constructed. Secondly, to enhance feature reuse and propagation, on the basis of YOLOv3 and Dense-Blocks, the YOLOv3-dense network was utilized for insulator detection. To improve detection accuracy for different sized insulators, a structure of multiscale feature fusion was adapted to the YOLOv3-dense network. To obtain abundant semantic information of upper and lower layers, multilevel feature mapping modules were employed across the YOLOv3-dense network. Finally, the YOLOv3-dense network and compared networks were trained and tested on the testing set. The average precision of YOLOv3-dense, YOLOv3, and YOLOv2 were 94.47%, 90.31%, and 83.43%, respectively. Experimental results and analysis validate the claim that the proposed YOLOv3-dense network achieves good performance in the detection of different size insulators amid diverse background interference.

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“…Until most recently, studies have been underway on object detection problems based on the YOLO architecture. Xu et al [28] proposed an improved YOLOv3 using DenseNet and detected the multi-scale remote sensing targets to address the problem of poor detection performance for small objects, which is a weakness of the YOLO architecture. In light-weight YOLO for edge-computing, Wong et al [29] proposed the YOLO Nano model by human-machine collaboration design strategy, which is 8.3 times smaller model size, 10.7% higher accuracy on VOC 2007 dataset, and 17% lower operations when compared with YOLOv3-tiny.…”

Section: Introductionmentioning

confidence: 99%

Design of a Scalable and Fast YOLO for Edge-Computing Devices

Han

Lee

Lim

et al. 2020

Sensors

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With the increase in research cases of the application of a convolutional neural network (CNN)-based object detection technology, studies on the light-weight CNN models that can be performed in real time on the edge-computing devices are also increasing. This paper proposed scalable convolutional blocks that can be easily designed CNN networks of You Only Look Once (YOLO) detector which have the balanced processing speed and accuracy of the target edge-computing devices considering different performances by exchanging the proposed blocks simply. The maximum number of kernels of the convolutional layer was determined through simple but intuitive speed comparison tests for three edge-computing devices to be considered. The scalable convolutional blocks were designed in consideration of the limited maximum number of kernels to detect objects in real time on these edge-computing devices. Three scalable and fast YOLO detectors (SF-YOLO) which designed using the proposed scalable convolutional blocks compared the processing speed and accuracy with several conventional light-weight YOLO detectors on the edge-computing devices. When compared with YOLOv3-tiny, SF-YOLO was seen to be 2 times faster than the previous processing speed but with the same accuracy as YOLOv3-tiny, and also, a 48% improved processing speed than the YOLOv3-tiny-PRN which is the processing speed improvement model. Also, even in the large SF-YOLO model that focuses on the accuracy performance, it achieved a 10% faster processing speed with better accuracy of 40.4% mAP@0.5 in the MS COCO dataset than YOLOv4-tiny model.

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Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection

Cited by 94 publications

References 51 publications

Weak and Occluded Vehicle Detection in Complex Infrared Environment Based on Improved YOLOv4

Weak and Occluded Vehicle Detection in Complex Infrared Environment Based on Improved YOLOv4

Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference

Design of a Scalable and Fast YOLO for Edge-Computing Devices

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