Multi‐scale object detection by bottom‐up feature pyramid network
Zhao Boya, Zhao Bao-jun, Tang Linbo, Chen Wu · The Journal of Engineering · 2019
The deep neural networks has been developed fast and shown great successes in many significant fields, such as smart surveillance, self‐driving and face recognition. The detection of the object with multi‐scale and multi‐aspect‐ratio is still the key problem. In this study, the authors propose a bottom‐up feature pyramid network, coordinating with multi‐scale feature representation and multi‐aspect‐ratio anchor generation. Firstly, the multi‐scale feature representation is formed by a set of fully convolutional layers which is catenated after the backbone network. Secondly, in order to link the multi‐scale feature, the deconvolutional layer is involving after each multi‐scale feature map. Thirdly, to tackle the problem of adopting object with different aspect ratios, the anchors on each multi‐scale feature map are generated by six shapes. The proposed method is evaluated on PASCAL visual object detection dataset and reach the accuracy of 80.5%.