The paper proposes a DRM-YOLOv8n small target detection algorithm to enhance the accuracy of image detection for PCB defect detection. Firstly, the deformable convolutional module is employed to address the issue of small-scale feature extraction, thereby enhancing the backbone network’s capability in extracting crucial features and improving detection accuracy. Secondly, a receptive field attention Convolution (RFAConv) is introduced into the Neck structure to improve model positioning accuracy in complex scenes and enhance network performance and efficiency. Lastly, the MPDIoU loss function is utilized to optimize the original network loss function, resulting in improved boundary box regression accuracy and model convergence ability. Compared with YOLOv8n, our proposed algorithm achieves significant improvements in average precision value (mAP), with mAP50% increasing from 87.2% to 94.5% and mAP50:95% increasing from 60.9% to 65.8%, respectively - representing a 7.3% and 4.9% improvement over YOLOV8n.