Athlete target detection method in dynamic scenario based on nonlinear filtering and YOLOv5

Dong, Weijia; Pan, Lingyan; Zhang, Qi; Zhang, Wentai

doi:10.1016/j.aej.2023.09.061

Cited by 2 publications

(1 citation statement)

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“…The input layer involves image preprocessing, including mosaic data augmentation, adaptive image scaling, and adaptive anchor box adjustments. The output layer is responsible for detecting the categories and positions of target objects and is primarily composed of loss functions and non-maximum suppression techniques [20,21].…”

Section: Yolov5 Network Frameworkmentioning

confidence: 99%

A Lightweight Model for Real-Time Detection of Vehicle Black Smoke

Chen,

Wang,

Zhai

2023

Sensors

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This paper discusses the application of deep learning technology in recognizing vehicle black smoke in road traffic monitoring videos. The use of massive surveillance video data imposes higher demands on the real-time performance of vehicle black smoke detection models. The YOLOv5s model, known for its excellent single-stage object detection performance, has a complex network structure. Therefore, this study proposes a lightweight real-time detection model for vehicle black smoke, named MGSNet, based on the YOLOv5s framework. The research involved collecting road traffic monitoring video data and creating a custom dataset for vehicle black smoke detection by applying data augmentation techniques such as changing image brightness and contrast. The experiment explored three different lightweight networks, namely ShuffleNetv2, MobileNetv3 and GhostNetv1, to reconstruct the CSPDarknet53 backbone feature extraction network of YOLOv5s. Comparative experimental results indicate that reconstructing the backbone network with MobileNetv3 achieved a better balance between detection accuracy and speed. The introduction of the squeeze excitation attention mechanism and inverted residual structure from MobileNetv3 effectively reduced the complexity of black smoke feature fusion. Simultaneously, a novel convolution module, GSConv, was introduced to enhance the expression capability of black smoke features in the neck network. The combination of depthwise separable convolution and standard convolution in the module further reduced the model’s parameter count. After the improvement, the parameter count of the model is compressed to 1/6 of the YOLOv5s model. The lightweight vehicle black smoke real-time detection network, MGSNet, achieved a detection speed of 44.6 frames per second on the test set, an increase of 18.9 frames per second compared with the YOLOv5s model. The mAP@0.5 still exceeded 95%, meeting the application requirements for real-time and accurate detection of vehicle black smoke.

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Section: Yolov5 Network Frameworkmentioning

confidence: 99%