Abstract
The detection method based on anchor-free not only reduces the training cost of object detection, but also avoids the imbalance problem caused by an excessive number of anchors. However, these methods only pay attention to the impact of the detection head on the detection performance, thus ignoring the impact of feature fusion on the detection performance. In this article, we take pedestrian detection as an example and propose a one-stage network Cascaded Cross-layer Fusion Network (CCFNet) based on anchor-free. It consists of Cascaded Cross-layer Fusion module (CCF) and novel detection head. Among them, CCF fully considers the distribution of high-level information and low-level information of feature maps under different stages in the network. First, the deep network is used to remove a large amount of noise in the shallow features, and finally, the high-level features are reused to obtain a more complete feature representation. Secondly, for the pedestrian detection task, a novel detection head is designed, which uses the global smooth map (GSMap) to provide global information for the center map to obtain a more accurate center map. Finally, we verified the feasibility of CCFNet on the Caltech and CityPersons datasets.
Highlights
Pedestrian detection is a crucial but challenging task in computer vision and multimedia, which has been applied in various fields
The main contributions of this work are summarized as follows: (1) We propose a novel Cascaded Cross-layer Fusion module (CCF) to reduce the noise information in the shallow features through high-level semantic information, and at the same time reuse high-level semantic information to strengthen the high-level semantic information in the final feature map; (2) The center map provides the confidence of each object center point, but the confidence is obtained from local information
We proposed Cascaded Cross-layer Fusion module (CCF), which combines deep semantics and shallow details to obtain features, which will obtain more contextual semantic information
Summary
Pedestrian detection is a crucial but challenging task in computer vision and multimedia, which has been applied in various fields. Detection methods [1,2,3,4,5,6] show that directly using the features of the backbone output is not conducive to the detection of small objects in the image. Recent detection methods show that obtaining high-resolution and high-quality feature representations is the key to improving detection results. The low-level features of the backbone contain accurate small object information, while the high-level features contain accurate large object information. How to more effectively integrate the characteristics of different stages has been the focus of research on pedestrian detection in recent years
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