An improved anchor-free object detection method applied in complex scenes based on SDA-DLA34

Kun Sun, Yifan Zhen, Bin Zhang, Zhenqiang Song

Abstract: The anchor-free object detection CenterNet has the problems that the utilization rate of detected object features is low, which is difficult to detect morphological changes and blurred edge objects, susceptible to interference from irrelevant information in complex backgrounds. To solve the problems above, we propose a novel anchor-free method called SDA-DLA34 in this paper. First, to solve the problem that morphological changes and blurred edge objects are difficult to detect, it is proposed that to introduce a series of deformable convolution to replace the ordinary convolution in DLA34, which effectively improve the network perception ability of morphological changes and blurred edge objects. Second, to solve the problem of low utilization of object features, it is proposed that to introduce the soft pooling layers to replace max pooling layers in the down-sampling process of DLA34, which could reduce the loss of object feature information, especially small objects. Finally, in order to pay more attention to the key information, reducing the influence of background and other irrelevant information, it is proposed to introduce attention mechanism in DLA34 to enhance the ability of the network to extract key features of the object. Experiments on MS COCO and Pascal VOC datasets have been conducted, the results show that the SDA-DLA34 is superior to to the current mainstream methods. Compared with the DLA34, the mAP, AP0.5 and AP0.75 of SDA-DLA34 increase by 8.1%, 8.0% and 6.7% respectively.