To realize automated grasping of randomly placed C-type circlips in automotive and mechanical assembly processes, this paper proposes an identification method based on the improved YOLOv11 algorithm and an exclusive association strategy. First, a detail enhancement convolution (DEConv) module is introduced into the C3k2 module of the YOLOv11 network to enhance the ability to capture the edges and high-frequency details of the circlips; Second, the parallel patch-aware attention (PPA) module is adopted in the detection head instead of the traditional convolution, and through multi-branch fusion and channel-spatial attention mechanism, it adaptively focuses on the key areas of small targets; Finally, the normalized Wasserstein distance (NWD) loss function is used instead of the IoU loss to alleviate the gradient disappearance problem in the positioning of small targets and improve the model’s detection accuracy. Based on precise detection, this paper innovatively proposes a circlip-hole association strategy based on multi-level spatial constraints, achieving accurate pairing of randomly placed circlips through bounding box-inclusive attribution judgment, distance screening, and selection of the best circlip-pair. Experimental results show that compared with the original YOLOv11 model, our model improves the mAP50 and mAP50-95 significantly by 5.7% and 3.3%, respectively; its association accuracy reaches above 95%, and the average processing time is 142 ms. Both in terms of accuracy and speed, our model meets the requirements of industrial real-time grasping and provides reliable prior information for the subsequent precise clamping of the robotic arm.