REAL-TIME OBJECT DETECTION BY A MULTI-FEATURE FULLY CONVOLUTIONAL NETWORK

Prior work on object detection depends on region proposals to guide the search for object instances. Generally, several thousand proposals must be processed, thus hurting the detection efficiency. In this paper, we propose a new model free from region proposals for object detection which treats detection task as a regression problem. To improve small-size object detection and localization, we employ the deep hierarchical features extracted from convolutional neural networks (CNNs). The hierarchical architecture combines appearance information from a shallow layer with semantic information from a deep layer. Our approach can predict bounding boxes and class probabilities simultaneously from a full input image. We transfer a classification network called Darknet into fully convolutional network and fine-tune it for the detection task. Experiments on PASCAL VOC dataset demonstrate that our approach outperforms other detection models.