A Rich Feature Fusion Single-Stage Object Detector

Single-stage object detectors are quick and highly accurate. Based on the way training model is developed, single-stage object detectors either adopt a training model based on a pre-trained backbone network model, or a model trained from the scratch. The pre-trained backbone network model is associated with the propagation sensitivity both in classification and detection. This leads to deviations in learning goals, and results in an architecture which is limited by the classification network, hence not easy to modify. Training from the scratch is not as efficient as using a pre-trained network, mainly due to the limitations of the predefined network system. In this paper, we combine these two approaches to overcome the above-mentioned shortcomings. In our proposed method a top-down concatenated feature pyramid is built upon a basic FSSD network. The experiments in this paper are conducted on MS COCO and PASCAL VOC data sets. Moreover, we apply VGG16 as the backbone network to further indicate the effectiveness of our proposed method which reaches 33.1 AP on MS COCO benchmark.