基于YOLOv7-SEFNet的碎片群目标检测算法研究OA

In the context of high-speed photography as a non-contact method for fragment cluster-behind-target parameter testing,fragment cluster target detection serves as a critical step in parameter testing.To address the low contrast and unclear targets in fragment cluster images,this paper proposes a multi-scale fusion image enhancement algorithm combining Gamma correction and an unsharp mask to improve brightness and detail of fragment images.To address issues such as missed and false detections in small target detection,this paper introduces the fine-sensitive YOLOv7 algorithm(YOLOv7-SEFNet),which replaces CIoU loss function with SIoU for better bounding box regression accuracy,constructs a small target detection layer to improve the network's adaptability to minute features,and integrates an attention mechanism module to enhance the weighting focus on small targets.Experiments show that YOLOv7-SEFNet improves the recall rate by 3.86%,the precision rate by 3.09%,and the average precision rate by 1.77%over the original YOLOv7.When compared to networks ranging from YOLOv4 to YOLOv13,YOLOv7-SEFNet demonstrates superior overall performance in the task of fragment target detection,effectively enhancing the detection accuracy for images of fragment cluster-behind-target.