Research on Underwater Small Target Detection Algorithm Based on Improved YOLOv7
Research on target detection has always been a challenge for underwater small target detection.To address the issues of high miss detection rate and poor underwater scene recognition in underwater small target detection tasks,an improved underwater small target detection technique based on YOLOv7 is proposed.In order to achieve the accuracy and balance the detection speed,the YOLOv7 network is adopted as the basic network.By fusing the SENet attention mechanism,enhancing the FPN network topology,and incorporating the EIoU loss function,the crucial feature information of small targets is concentrated in the network to increase the detection accuracy while reducing the complexity of the model.Through simulation tests,the indexes of mAP and P as well as R are confirmed on the test set,and compared with other conventional target detection techniques,the results show that the enhanced algo-rithm is superior to the competing networks and successfully improves the detection accuracy on the test set.
YOLOv7underwater small target detectionattention mechanismFPNEIoU
万方数据
维普
