Design of Small Target Detection Algorithm in Motion Scenes
Mobile devices often face some problems such as image blur,low quality,and small target volume in sports scenes,leading to frequent false or missed detection of targets.Meanwhile,the existing models have a large num-ber of parameters and cannot meet the requirements of real-time performance.A YOLOv5s-C algorithm model is proposed to address these issues.Firstly,a coordinate attention mechanism is introduced into the backbone network to enhance the model's perception of detail related channel features,thereby improving the model's ability to locate and recognize tar-gets,especially for the blurred images and small targets.Secondly,in the feature enhancement network(Neck),a hybrid convolutional GSConv and a weighted bidirectional feature pyramid network Bi-FPN are used to obtain global con-text and information at different scales,thereby enhancing the model's ability to detect small targets in image blur situa-tions.Finally,EIoU Loss is introduced as the bounding box regression loss function to accelerate the convergence speed of the model and improve its detection accuracy.The test results show that in the publicly available COCO2017 dataset,the YOLOv5s-C algorithm model has a 29%reduction in parameter count compared to the original model,map@0.5 0.95 increased by 1.8%,map@0.5 improved by 2.3%,significantly reducing false positives and missed detections for small targets.In the case of a batch size of 32,the speed of the model reached 190.3 f/s.The YOLOv5s-C algorithm model has shown excellent performance and broad application prospects in small object detection in motion scenes.
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