Improved YOLO v5 for Railway PCCS Tiny Defect Detection

Zhao, Tianke; Wei, Xiaonan; Yang, Xuewu

doi:10.1109/icaci55529.2022.9837504

Cited by 7 publications

(2 citation statements)

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“…Howard et al [17] proposed to combine LRM and Focal loss in YOLOv5 to improve the average accuracy. Zhao et al [18] used the ghost module to reduce the parameters and thus further improve the detection speed. A series of valuable works have contributed to the development of YOLO algorithm.…”

Section: Related Workmentioning

confidence: 99%

An improved method MSS-YOLOv5 for object detection with balancing speed-accuracy

et al. 2023

Front. Phys.

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For deep learning-based object detection, we present a superior network named MSS-YOLOv5, which not only considers the reliability in complex scenes but also promotes its timeliness to better adapt to practical scenarios. First of all, multi-scale information is integrated into different feature dimensions to improve the distinction and robustness of features. The design of the detectors increases the variety of detection boxes to accommodate a wider range of detected objects. Secondly, the pooling method is upgraded to obtain more detailed information. At last, we add the Angle cost and assign new weights to different loss functions to accelerate the convergence and improve the accuracy of network detection. In our network, we explore four variants MSS-YOLOv5s, MSS-YOLOv5m, MSS-YOLOv5x, and MSS-YOLOv5l. Experimental results of MSS-Yolov5s show that our technique improves mAP on the PASCAL VOC2007 and PASCAL 2012 datasets by 2.4% and 2.9%, respectively. Meanwhile, it maintains a fast inference speed. At the same time, the other three models have different degrees of performance improvement in terms of balancing speed and precision in challenging detection regions.

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Section: Related Workmentioning

confidence: 99%

An improved method MSS-YOLOv5 for object detection with balancing speed-accuracy

et al. 2023

Front. Phys.

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“…In [22], the backbone network was replaced from ResNet50 to MobileNetV1 with modified RetinaNet, which effectively improved real-time network performance. [23] applied the focal loss in YOLOv5 to increase the accuracy in detecting tiny targets. In [24], Cai et al used PAN++ and improved YOLOv4 with five scale detection layers, which effectively improved the detection of small target objects.…”

Section: Introductionmentioning

confidence: 99%

YOLO‐DFD: A Lightweight Method for Dog Feces Detection Based on Improved YOLOv4

Guo-zhan

Feng

et al. 2023

Journal of Sensors

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Computer vision has been integrated into people’s daily lives, but mainstream target detection algorithms deployed to embedded devices with limited hardware resources are difficult to meet the task requirements in terms of real time and accuracy. So we proposed YOLO-DFD, a light detection algorithm based on improved YOLOv4 to solve the problem of dog feces in our living environment. The main improvement strategies are as follows: the YOLOv4 backbone network is replaced with MobileNetV3, and the 3 ∗ 3 convolutions in the feature enhancement network are replaced by depthwise separable convolutions to further reduce the number of parameters. To enhance the accuracy of target detection, we introduced the convolutional block attention module (CBAM) in neck network, and the complete intersection over union (CIoU) loss of YOLOv4 is replaced with the SCYLLA intersection over union (SIoU) loss to reduce false detection rate. In this paper, the dataset we used was made up of pictures of dog feces taken in life. For the self-made dog feces dataset, we used data enhancement technology to expand it. The training result shows that the average precision (AP) has reached 98.66%. While maintaining detection performance, the parameter of YOLO-DFD is reduced by 82% and FPS increases 14 compared to YOLOv4. And YOLO-DFD has a lower parameter quantity and a smaller calculation than other algorithms, making it easier to deploy on embedded devices to clean dog feces.

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Track Obstacle Real-Time Detection of Underground Electric Locomotive Based on Improved YOLOX

Fan²,

Xiong

et al. 2023

Lecture Notes in Computer Science

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Improved YOLO v5 for Railway PCCS Tiny Defect Detection

Cited by 7 publications

References 0 publications

An improved method MSS-YOLOv5 for object detection with balancing speed-accuracy

An improved method MSS-YOLOv5 for object detection with balancing speed-accuracy

YOLO‐DFD: A Lightweight Method for Dog Feces Detection Based on Improved YOLOv4

Track Obstacle Real-Time Detection of Underground Electric Locomotive Based on Improved YOLOX

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