基于改进YOLOv5的低光照图像识别研究

Research on low-illumination image recognition based on improved YOLOv5

针对现有识别方法存在的图像信息熵低、识别正确率不足等问题,文章采用改进YOLOv5算法进行了低光照图像识别,通过增强局部暗区亮度、提升图像整体亮度及聚合多尺度特征等手段构建了一个低光照目标识别架构。该架构可利用卷积神经网络优化目标感兴趣区域(ROI)尺度,以确保图像细节不丢失。实验结果显示,基于5000份样本,改进方法的信息熵值提升至6.5,识别正确率高达98%,显著优于对照组的68%~72%。这一方法实现了精准的低光照图像识别,有效提高了图像可见性,为低光照环境下的图像识别任务提供了有效的解决方案。

In response to the problems of low image information entropy and insufficient recognition accuracy in existing recognition methods,this article adopts an improved YOLOv5 algorithm for low light image recognition.By enhancing the brightness of local dark areas,improving the overall brightness of the image,and aggregating multi-scale features,a low light target recognition architecture is constructed.This architecture can utilize convolutional neural networks to optimize the scale of the target region of interest(ROI)to ensure that image details are not lost.The experimental results showed that based on 5000 samples,the information entropy value of the improved method increased to 6.5,and the recognition accuracy reached 98%,significantly better than the 68%~72%of the control group.This method achieves precise low light image recognition,effectively improving image visibility and providing an effective solution for image recognition tasks in low light environments.