Enhanced Small Target Recognition with Lightweight YOLOv5 in Low-Res Images

The improvement of deep learning algorithms for small object detection in low-resolution images remains a significant challenge. Time-of-Flight (TOF) sensors can replace cameras for indoor use, offering privacy protection but facing limitations in accurately detecting small objects. This paper proposes an algorithm that combines Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalizer (CLAHE) for image enhancement, further optimizing the Infrared (IR) images and depth maps collected by TOF sensors. Simultaneously, using the enhanced IR images and fused depth maps data based on the improved lightweight YOLOv5n algorithm, the performance of the proposed algorithm is validated. Experimental results demonstrate that the novel algorithm outperforms existing methods, with average precision scores of 98.4% and 72.1%, respectively.