Deep learning based multi-branch structure detection network
Pengfei Li · 2023
Convolutional neural networks perform extremely well in current image classification tasks based on deep learning. In our research experiment, we found a flaw and made a simple but extremely effective improvement. In this article, we used the traditional ResNet and VGG16 as the benchmark networks. By parallelizing multiple networks into a multi-branch structure model, we designed a multi-branch structure classification header, arranged in parallel order into a whole model, and sent the entire image into the detection header of the multi-branch structure. Through different convolution kernels, we obtained the category scores of multiple categories, and output the highest score. Adding a multi-branch architecture as an external module to the target detection network has been experimentally found to improve performance. Our experiments on cifar-10, cifar-100, and ImageNet 2012 datasets have shown that our method can achieve extremely high classification network prediction accuracy under extreme conditions. Under conventional conditions, the accuracy rate also reaches 97%. Applying related modules to target detection networks also has relatively improved performance.