Iterative Fusion and Dual Enhancement for Accurate and Efficient Object Detection

Abstract

Single Shot Multibox Detector (SSD) uses multi-scale feature maps to detect and recognize objects, which considers the advantages of both accuracy and speed, but it is still limited to detecting small-sized objects. Many researchers design new detectors to improve the accuracy by changing the structure of the multi-scale feature pyramid which has proved very useful. But most of them only simply merge several feature maps without making full use of the close connection between features with different scales. In contrast, a novel feature fusion module and an effective feature enhancement module is proposed, which can significantly improve the performance of the original SSD. In the feature fusion module, the feature pyramid is produced through iteratively fusing three feature maps with different receptive fields to obtain contextual information. In the feature enhancement module, the features are enhanced along the channel and spatial dimensions at the same time to improve their expression ability. Our network can achieve 82.5% mean Average Precision (mAP) on the VOC 2007 [Formula: see text], 81.4% mAP on the VOC 2012 [Formula: see text] and 34.8% mAP on COCO [Formula: see text]-[Formula: see text]2017, respectively, with the input size [Formula: see text]. Comparative experiments prove that our method outperforms many state-of-the-art detectors in both aspects of accuracy and speed.