An efficient object detection framework with modified dense connections for small objects optimizations

Abstract

Object detection frameworks for small objects are increasingly demanded in some specific fields such as high-speed object tracking and remote sensing image recognition. In this paper, we propose an efficient object detection framework with modified dense connections for small objects. In order to improve both the detection accuracy and speed for small objects, the proposed framework constructs a convolutional neural network by using modified dense and residual cross-layer connections between multi-scale convolutional layers to extract deep features effectively. Based on the modified dense structure, a hybrid-scale feature fusion method is proposed to concatenate the multi-channel high-dimensional features and performs cross-entropy calculation and regression prediction. By using this method, this framework not only improves the detection accuracy for small objects significantly, but also improves the overall detection accuracy and optimizes the network parameters to reduce the detection time greatly. The experimental results show that the proposed framework achieves 90.6% mAP for small objects on a public ship dataset which is 25.2% more than SSD-VGGNet. Due to the detection efficiency for small objects, it improves the overall detection accuracy and detection speed by 9% and 40% respectively while about 70% network parameters are reduced.