基于改进YOLOv7的水下小目标检测算法研究

Research on Underwater Small Target DetectionAlgorithm Based on Improved YOLOv7

摘要:

目标检测研究一直是水下小目标检测的难题。针对水下小目标检测任务漏检率高、水下场景识别效果差的问题,提出一种利用YOLOv7改进的水下小目标检测技术。为了达到准确率的同时兼顾高检测速度,采用YOLOv7网络作为基础网络。该网络通过融合SENet注意力机制、增强FPN网络拓扑、结合EIoU损失函数,集中小目标更关键的特征信息,提高检测精度,同时降低模型复杂度。通过模拟测试,在测试集上确认了mAP、P和R指标,并与其他传统目标检测技术进行了对比。结果表明,增强的算法优于竞争网络,并成功提高了测试集的检测精度。

Abstract:

Target detection research has always been difficult when it comes to small target detection in underwater situations. To address the issues of a high miss detection rate and poor underwater scene recognition in underwater small target detection tasks, an improved underwater small target detection technique utilizing YOLOv7 is proposed. To achieve the accuracy rate while considering the high detection speed, the YOLOv7 network is used as the basic network. The network concentrates more crucial feature information of small targets to increase detection accuracy while reducing model complexity by merging the SENet attention mechanism, enhancing the FPN network topology, and incorporating the EIoU loss function. Through simulation tests, the mAP, P, and R metrics are confirmed on the test set and contrasted with other conventional target detection techniques. The outcomes demonstrate that the enhanced algorithm outperforms competing networks and successfully raises detection accuracy on the test set.