Efficient Small-Object Detection in Airport Surface Based on Maintain Feature High Resolution

Abstract

Detection of small objects is a challenge in computer vision, especially in the application of airport surface surveillance. Operation of convolution is the key to losing the semantic information of small objects. In this paper, a high-resolution network is designed to maintain the feature information of small objects. Channel restructuring is used to enlarge the feature map and merge it with the shallow feature map to enhance the feature position information, rather than bilinear interpolation upsampling in traditional methods. Experiments show that, in the self-built airport surface dataset, compared with the mainstream object detector Fully Convolutional One-Stage Object Detector, RetinaNet, and other methods, the proposed method can reach the state-of-the-art in various precision indexes. In terms of model size and number of parameters, the proposed method is significantly smaller than Faster Region-Convolutional Neural Networks. Under the same input size, compared with You Only Look Once v3, the method presented in this paper can obtain a lower number of parameters and a faster inference speed. In the case of using GTX1080Ti, our method can reach 76.7 frames per second.