Research On Object Detection Algorithm Based On YOLOv3

Object detection is the basis of a large number of high-level vision tasks such as image analysis and understanding.As one of the most popular algorithms in object detection technology,YOLOv3 has a wide range of applications due to its good generalization ability.In order to meet the requirements of algorithm deployment for terminal equipment with high detection accuracy and low memory usage,this dissertation studies the object detection algorithm from two aspects:improving detection accuracy and compressing model parameters,and finally improves it based on YOLOv3.In terms of improving the detection accuracy,we start from two perspectives:the attention method that can be embedded in the model and the data augmentation method used in model training.For attention,combined with the detection principle of the YOLOv3 algorithm,the anchor frame information is introduced into the attention method as prior knowledge,and an improved attention method is obtained.Comparative experiments show that compared with the original YOLOv3 algorithm,the YOLOv3 algorithm using the improved attention method has 0.7%and 0.4%mAP improvements on the VOC2007 and VOC2012 data sets,respectively.For data augmentation,the two methods of MixUp and Mosaic are combined to get an improved data augmentation method.In order to verify the general effect of the improved method,the benchmark network PyramidNet is used to conduct comparative experiments on the CIFAR-100 and CIFAR-10 data sets.The results show that after the improved data augmentation method is used during model training,the Top-1 error rate is compared to Before use,it was reduced by 2.47%and 1.31%,respectively.In terms of model compression,an improved strategy is proposed to filter out the convolution kernels with a proportion of zero activations greater than the threshold before performing FPGM pruning.In order to verify the general effect of the improved method,a comparative experiment was conducted on the CIFAR-10 data set with ResNet-56 as the benchmark network.The results show that after ResNet-56 uses the improved FPGM pruning strategy,FLOPs are reduced by about 53%compared to before pruning,and the accuracy rate is increased by about 0.05%compared with that before pruning.Based on the improved methods proposed above,comparative experiments are carried out on the MS COCO data set.The results show that the improved attention method is added to the network structure of the YOLOv3 algorithm,and the improved data augmentation method is used during training.After the inference model is obtained,the improved FPGM pruning strategy is used for pruning.The final AP can reach 37.9%,compared with Before improvement,it increased by 6.9%,and the amount of network parameters was reduced from 59.6M to 33.1M.Although the improved YOLOv3 algorithm has some gaps compared with other algorithms with excellent performance,it still has a large performance improvement compared to YOLOv3 itself.