CDC-YOLO for Gear Defect Detection: Make YOLOv8 Faster and Lighter on RK3588

Defect detection on the gear surface is crucial for preventing faults in mechanical systems. However, most detection models are not extremely effective on embedded platforms. To address this issue, we present a lightweight detection model called CDC-YOLO, which is based on YOLOv8 and specifically designed for embedded platforms. It utilizes our proposed CDC module as a residual structure for extracting multi-scale features, allowing for better adaptation to different platforms. Additionally, we achieve model lightweighting by using a dual convolutional architecture. Experimental results on both computers and embedded platforms demonstrate that our proposed method outperforms the baseline YOLOv8.