Parts recognition method based on improved Faster RCNN

Aiming at the problems of mutual occlusion between assembly parts, different poses of parts, external light intensity, missed detection of small targets and low detection accuracy of traditional machine vision detection and recognition methods, a parts recognition method based on improved faster recurrent convolutional neural network (RCNN) was proposed. Firstly, the ResNet101 network with better feature extraction was used to replace VGG16 feature extraction network in original Faster RCNN model. Secondly, for the original candidate region network, the two new anchors were added and the aspect ratio of candidate frame was reset to obtain the 15 anchors with different sizes. Then, aiming at the missed detection problems caused by deleting the candidate frame in which the Intersection-over-Union (IoU) was greater than the threshold in traditional non-maximum suppression (NMS) method, the Soft-NMS method was used to replace the traditional NMS method, so as to reduce the missed detection problems in dense regions. Finally, in training model stage, the multi-scale training strategy was adopted to reduce the missed detection rate and improve the accuracy of the model. The experimental results show that the improved Faster RCNN model can achieve 96.1% accuracy, which is 4.6% higher than the original model, and can meet the recognition and detection of parts in complex conditions such as strong illumination and water stain interference.