In certain fixed industrial application scenarios， the tolerance of the target detection algorithms to miss detection is very low. However， while increasing the recall， some non-overlapping virtual frames are likely to be regularly generated around the target. The traditional Non-Maximum Suppression （NMS） strategy has the main function to suppress multiple repeated detection frames of the same target， and cannot solve the above problem. To this end， an anisotropic NMS method was designed by adopting different suppression strategies for different directions around the target， and was able to effectively eliminate the regular virtual frames. The target shape and the regular virtual frame in a fixed industrial scene often have a certain relevance. In order to promote the accurate execution of anisotropic NMS in different directions， a ratio Intersection over Union （IoU） loss function was designed to guide the model to fit the shape of the target. In addition， an automatic labeling dataset augmentation method was used for the regular target， which reduced the workload of manual labeling and enlarged the scale of the dataset. Experimental results show that the proposed method has significant effects on the roll groove detection dataset， and when it is applied to the YOLO （You Only Look Once） series of algorithms， the detection precision is improved without reducing the speed. At present， the algorithm has been successfully applied to the production line of a cold rolling mill that automatically grabs rolls.