A Multi-Scale Detector Based on Attention Mechanism

After the two-stage detector is first introduced and popularized by R-CNN, two-stage detectors have achieved great performance, but there are still many problems. 1. FPN tries to use different level features to deal with the scale variance problem in object detection, but lacks the screening of information, leading to important information not protruding and introducing interference; 2. Although CNN realizes the automation of feature extraction, there are still many components that need manual design, such as loss functions, etc., how to choose suitable loss function for different stages are still to be explored. To overcome these issues, we introduce attention mechanism to FPN and propose a more effective feature fusion method for it. Besides, we explore the selection criteria about choosing loss function in each stage and find combining Smooth L1 loss function with the new loss function focused on inliers such as Balanced Smooth L1 yield better results than only using a single loss function. Based on them, we propose Attention Feature Pyramid Networks(AFPN) Detector and train with different loss functions. Experiments show that our method achieves 1.1 points AP improvement than FPN Faster R-CNN on MS-COCO.