In order to solve the problem of most current research work only considering the factors affecting pedestrian interaction， based on State-Refinement Long Short-Term Memory （SR-LSTM） and attention mechanism， a Social-Interaction Generative Adversarial Network （SIGAN） for pedestrian trajectory prediction was proposed， namely SRA-SIGAN， where GAN was utilized to learn movement patterns of target pedestrians. Firstly， SR-LSTM was used as a location encoder to extract the information of motion intention. Secondly， the influence of pedestrians in the same scene was reasonably assigned by setting the velocity attention mechanism， thereby handling the pedestrian interaction better. Finally， the predicted future trajectory was generated by the decoder. Experimental results on several public datasets show that the performance of SRA-SIGAN model is good on the whole. Specifically on the Zara1 dataset， compared with SR-LSTM model，the Average Displacement Error （ADE）and Final Displacement Error （FDE）of SRA-SIGAN were reduced by 20.0% and 10.5%，respectively；compared with the SIGAN model，the ADE and FDE of SRA-SIGAN were decreased by 31.7% and 24.4%，respectively.

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.