DCD-Net: Dense Component Detection Network for Printed Circuit Board Assembly Inspection

Electronic components in the printed circuit board assembly (PCBA) exhibit great diversities in categories, visual appearances and geometric dimensions. Efficient and precise detection for dense components is crucial and challenging for early quality inspection. Therefore, this paper proposes a novel dense component detection network (DCD-Net) established on a few improvements upon YOLO11-nano. First, a novel lighter GFConv block integrating Ghost convolution and a channel fusion operation is built to downsize the model, meanwhile enhancing the detection precision. Second, a creative C3DS module incorporating dynamic snake convolution is designed for excellent adaptability to diverse geometric forms and the large-scale variance of electronic components. Finally, FC-IoU loss involving the hard-easy sample distribution is proposed to facilitate the bounding box regression, boosting the positioning accuracy. The experiment results verify that the proposed DCD-Net achieves remarkable mean average precision of 91.6% ([email protected]) and 77.9% ([email protected]:0.95), exceeding 0.9% and 2.4% increments separately than YOLO11-nano. In addition, a 93 frames/s inference speed is achieved for inputs with 640 × 640 pixel resolution, demonstrating its notable potential to realize real-time industry applications. The code is available at https://github.com/lqin0818/DCD-Net/tree/master.