Aiming at the difficulty of occlusion and blur detection caused by complex detection scene in the process of wire harness terminal crimping, a wire harness terminal defect detection model ACS-YOLO based on improved YOLOv11 is proposed. The C2PSA_EFA module is designed in this model. Combining C2PSA with edge-enhanced feature attention, sobel operator is used to extract and fuse edge information to enhance the irregular defect capture ability of harness terminals. The attention scale sequence fusion module ASF-YOLO is introduced to improve the Neck part, and the multi-scale feature fusion mechanism is introduced to improve the detection ability of the model to the wire harness terminal defect features. The SlideLoss classification loss function is introduced to adjust the loss weight according to the difficulty of the sample, and the detection ability of the model to difficult samples is improved. The experimental results show that the accuracy, recall rate and mAP50 of the ACS-YOLO model are increased by 6.1%, 1.0% and 3.1% respectively compared with the original model, which can be effectively applied to the harness terminal defect detection task.