Multi-Scale Object Detection Method Based on Multi-Branch Parallel Dilated Convolution

Abstract

: Existing object detection algorithms only use a fixed size convolution kernel when extracting features, ignoring the difference in the receptive field of different scale features, which affects the detection ef-fect of different scale objects. To solve this problem, a multi-scale object detection network based on multi-branch parallel dilated convolution is proposed. Firstly, the basic network VGG-16 is used to extract the features of the image. Secondly, a multi-branch parallel dilated convolution is designed to extract multi-scale features to improve object detection ability of the network. Then, a non-local block is employed to integrate the global spatial information and enhance the context information. Finally, the object detection and location tasks are performed on feature maps with different scales. Experimental results on PASCAL VOC and MS COCO datasets demonstrate that the proposed method can effectively improve the detection accuracy of different scale objects and clearly improve the detection accuracy of small objects.