Abstract
This paper proposes a novel single shot network for object detection. The proposed network, termed IDNet, explores the strategies of the feature fusion to alleviate the scale variation problem in object detection. IDNet mainly consists of two feature fusion modules: an indirect feature fusion module (IF) and a direct feature fusion module (DF). The IF shares long-range dependencies within pyramidal layers and based on these information, IDNet learns to emphasize informative regions and suppress the less useful ones on each layer. The DF is a feature fusion strategy based on modified lateral connection inspired by feature pyramid networks (FPN). It utilizes the averaging operation to reduce the change of feature maps' order of magnitude during fusing features to further improve the performance for detecting small instances. Comprehensive experiments are performed and the results indicate the effectiveness of IDNet, which reaches 80.3 mAP on PASCAL VOC 2007 benchmark.