Inspection robots have become critical tools for roller detection in belt conveyors. However, the infrared images detected by these robots often suffer from low resolution and a low signal-to-noise ratio, thereby introducing higher requirements for target detection algorithms. In this study, we propose improvements to inspection robots for roller detection tasks in belt conveyors based on the YOLOv5 network. Inspired by DenseNet, we first introduce dense connection modules into the YOLOv5 network to enhance its feature extraction capabilities. We then introduce a Wise-IoU (WIoU) loss function to evaluate the quality of anchor rectangles and in turn improve network performance and generalization capabilities. Experimental evaluations on a dataset of infrared data collected by inspection robots on belt conveyors demonstrate that, compared with the original YOLOv5, the recall rate and mean average precision are improved by 2.4 percentage points and 1.5 percentage points, respectively (with the latter reaching 98%), while a recognition speed of 80 frame/s and model size of 15 MB are maintained. The improved inspection robot features a small size, fast speed, and high efficiency.