Multi-Target Detection with Larger Scale Difference

摘要

The main contribution of this article is to solve the problem of detection of larger scale differences. Aiming at the problem of small target detection, we can better use the underlying features of the convolution network to construct the hyper convolution feature to achieve better detection and recognition effect. For larger scale target, by dilated convolution operation, the context information of different scales can be integrated into high-level feature information according to different receptive fields. In this experiment, we introduce the lightweight convolutional network, SqueezeNet, as the basic feature network. The network has small size, fast training speed and strong expression ability. In the experiment environment of single Titan X GPU card, the distribution of the migrated dataset can be better studied by increasing the size of batch images during training. After the pre-training of the VOC dataset, the migration training was carried out in the remote sensing image dataset, and the mAP of the detection of the 12 targets reached 0.937205, which reached a better level of detection result.