Light source influence Matrix-YOLO: an object detection framework for low-visibility conditions

Contemporary object detection algorithms have achieved significant advancements in various fields. However, existing object detection algorithms suffer from reduced image clarity and increased noise under low visibility conditions, resulting in a decline in algorithm accuracy. To address this issue, we propose a novel Light Source Influence Matrix-YOLO (LSIM-YOLO) framework. we employ the LSIM algorithm to enhance the contrast of objects within the images. Conventional methods for object detection under low visibility conditions tend to overlook latent information within the images. To mitigate this, we introduce the se-ECA (Spatial Excitation and Channel Attention) mechanism to augment spatial information in features, which can enhance feature quality, thereby improving the performance of visual tasks under low visibility. Furthermore, we address the limitations of existing object detection networks in extracting features from low visibility images by replacing traditional strided convolution layers in the CNN architecture with Spatial-to-Depth Convolution (SPD-Conv). This compensates for the deficiency in utilizing fine-grained information during feature extraction. These efforts culminate in the development of the LSIM-YOLO model. Experimental results on the real dataset RTTS demonstrate a notable 3% improvement in average precision (AP), validating the feasibility and effectiveness of our improved detection framework.