FFBNet : Lightweight Backbone for Object Detection Based Feature Fusion Block

Abstract

Currently, most advanced object detectors equip with deep convolutional neural network (CNN) backbones(e.g., ResNet-101) for capturing strong feature representation, but which leads to suffer heavy computational burden. Inversely, some detectors based on lightweight backbones implement real-time processing while accuracy is often not satisfactory. In this paper, we explore an approach to construct a light yet powerful detector by using efficient lightweight backbone (e.g., MobileNet) with our proposed Feature Fusion Block (FFB), composed of Feature Aggregation Block (FAB) and Dense Feature Pyramid (DFP). The extensive experiments confirmed that our proposed Feature Fusion Block Network (FFBNet) is able to improve the accuracy significantly of MobileNet-SSD as well as maintain a close processing speed. Specifically, on Pascal VOC datasets, FFBNet achieves 73.54 mAP at speed of 185 FPS and surpasses MobileNet-SSD five points. Moreover, we apply VGG16 as backbone to further indicate the effectiveness of FFB, which reaches 80.2 mAP on Pascal VOC benchmark. Code is available at https://github.com/fanbinqi/FFBNet.