In response to the tight gear arrangement and the high accuracy required for recognition in gear transmission systems, this paper proposes an improved lightweight model for gear detection, called HPF-YOLO. Built upon YOLOv11, the model incorporates modules such as wavelet transform and multi-scale feature fusion, and optimizes key components like feature extraction and fusion. Experiments are conducted on a dedicated dataset simulating a gear transmission system. The results show that the proposed model outperforms the benchmark YOLOv11 in both accuracy and speed.

Gear; Wavelet convolution; Feature fusion; YOLO