MLFF: A Object Detector based on a Multi-Layer Feature Fusion

Abstract

A major challenge in the field of object detection is to design a model with high precision and high speed, which is necessary in real applications. In this paper, we study the problem of object detection, and propose a multi-layer feature fusion model for object detection, called MLFF. MLFF not only takes into account the rich location information and detail information of low-level features, but also considers the rich spatial information of high-level features. In MLFF, we develop three different components, Backbone, Neck and Head, to gradually extract and fuse low-level feature maps and high-level feature maps. In addition, CBAM attention mechanism is also used in feature fusion, which enables us to achieve more accurate feature extraction. Extensive experiments on PASCAL VOC2007 and VOC2012 datasets show that our MLFF model not only has the speed of one-stage object detection model but also has the detection precision of two-stage object detection model. Considering both precision and speed, MLFF outperforms state-of-the-art detection models significantly.