S-FEATURE PYRAMID NETWORK AND ATTENTION MODULE FOR SMALL OBJECT DETECTION

Because of the low resolution and limited information of small objects, and the computing resources are limited in practical applications, small object detection is still challenging. In order to improve the accuracy of small object detection, we propose a new method. It's included a shallow feature pyramid network with an information extraction block at the shallow features and fused multi-scale semantic information. Further, context information with attention mechanism is adopted to make object detection focus on the significant area. We are one of the top five teams in the Drone-vs-Bird Detection Grand Challenge. The detection ability of our method for small objects is much higher than classical one-stage and two-stage detectors. For limited computer resources, 300x300 inputs are used and the detection speed of 45 fps is reached by the proposed method, which can realize real-time object detection.