中南民族大学学报（自然科学版）2024，Vol.43Issue(6)：797-805,9.DOI:10.20056/j.cnki.ZNMDZK.20240610
基于膨胀卷积和参数重构的鱼类目标实时检测方法
Real-time fish object detection based on dilated convolution and parameter reconstruction
摘要
Abstract
To solve the problem that existing object detection methods are not ideal for fish object detection in complex scenes,a real-time fish object detection method based on expansion convolution and parameter reconstruction is proposed.Firstly,a four-branch fusion convolution structure is designed to expand the perceptual field of object detection and improve the effect of object detection by introducting a small number of parameters.Then a RepVGG(Reconstructed VGG)parallel auxiliary branch idea is introduced to use a complex model for feature learning in the training process,while the parameters in the BN(Batch Normalization)layer of the model and the 1×1 auxiliary branch are fused in the inference process,and the redundant parametric quantities in the training process are merged by using parameter reconstruction to ensure the low parametric number and real-time inference.The experiments are conducted based on YOLOv5s,and higher detection accuracy and recall are obtained compared to the original YOLOv5s,with a mean average precision(mAP)of 83.1%,surpassing the current mainstream object detection algorithms.The proposed algorithm has no significant reduction in detection speed compared with the original model,and the processing speed reaches 100 FPS,which ensures real-time detection of fish object while achieving high accuracy detection,and provides effective technical support for vision-based fish detection solutions.关键词
鱼类检测/计算机视觉/YOLOv5网络/膨胀卷积/参数重构/RepVGG模块Key words
fish detection/computer vision/YOLOv5 network/dilated convolution/parameter reconfiguration/RepVGG module分类
信息技术与安全科学引用本文复制引用
陈露露,臧兆祥,黄天星,李昭..基于膨胀卷积和参数重构的鱼类目标实时检测方法[J].中南民族大学学报（自然科学版）,2024,43(6):797-805,9.基金项目
国家自然科学基金资助项目(61502274) （61502274）
三峡大学水电工程智能视觉监测湖北省重点实验室开放基金资助项目(2015KLA08) （2015KLA08）
