MDFN: Multi-Scale Deep Feature Learning Network for Object Detection

Abstract

This paper proposes an innovative object detector by leveraging deep features learned in high-level layers. Compared with features produced in earlier layers, the deep features are better at expressing semantic and contextual information. The proposed deep feature learning scheme shifts the focus from concrete features with details to abstract ones with semantic information. It considers not only individual objects and local contexts but also their relationships by building a multi-scale deep feature learning network (MDFN). MDFN efficiently detects the objects by introducing information square and cubic inception modules into the high-level layers, which employs parameter-sharing to enhance the computational efficiency. MDFN provides a multi-scale object detector by integrating multi-box, multi-scale and multi-level technologies. Although MDFN employs a simple framework with a relatively small base network (VGG-16), it achieves better or competitive detection results than those with a macro hierarchical structure that is either very deep or very wide for stronger ability of feature extraction. The proposed technique is evaluated extensively on KITTI, PASCAL VOC, and COCO datasets, which achieves the best results on KITTI and leading performance on PASCAL VOC and COCO. This study reveals that deep features provide prominent semantic information and a variety of contextual contents, which contribute to its superior performance in detecting small or occluded objects. In addition, the MDFN model is computationally efficient, making a good trade-off between the accuracy and speed.

References in corpus (9)

• YOLOv3: An Incremental Improvement
• DSSD : Deconvolutional Single Shot Detector
• ParseNet: Looking Wider to See Better
• cuDNN: Efficient Primitives for Deep Learning
• Residual Networks Behave Like Ensembles of Relatively Shallow Networks
• Learning Depth from Single Images with Deep Neural Network Embedding Focal Length
• Accurate Single Stage Detector Using Recurrent Rolling Convolution
• Boosting Occluded Image Classification via Subspace Decomposition Based Estimation of Deep Features
• Unsupervised Deep Feature Transfer for Low Resolution Image Classification

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