Research on Improved Small Target Detection Algorithm Based on YOLOv5
To solve the problems of low accuracy and missed detection of small targets in scenarios by UAV,an improved small target detection algorithm DE_YOLOv5 based on YOLOv5 is proposed.The dataset of DE_YOLOv5 adopts VisDrone2019.The experiment includes two aspects.One is to introduce a decoupling joint mechanism,which reduces the impact of receptive field limitations on small target localization through independent center point prediction.mAP@0.5 of the initial model is 0.334,mAP@0.5 value is 0.344 after adding decoupling joint.The second is to replace the loss function with Focal-EIoU,and mAP@0.5 value is 0.351 after replacing the loss function.The experimental results show that DE_YOLOv5 can effectively improve the detection accuracy of small targets.
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