Lightweight Adaptive Feature Selection Network for Object Detection

doi:10.12068/j.issn.1005-3026.2021.09.004

Abstract

Abstract: There are some limitations of key technologies in small object detection， multi-category object detection， and especially lightweight models. To solve these problems， this paper proposes a lightweight adaptive feature selection network for object detection. The network is constructed to extract multi-scale features based on the feature pyramid. To alleviate the noise interference and preserve the detail information， a feature selection module composed of spatial adaptation and channel adaptation is designed. Specifically， the feature maps are filtered from spatial dimension， encoded and decoded in channel dimension for selecting the meaningful features adaptively. Besides， the classification network is constructed through depth-wise separable convolution to reduce the computational cost， improve detection efficiency and realize the lightweight version of the network. The detection accuracy on the PASCAL VOC 2007 dataset is 77.7% in mean average precision(mAP)， and the detection speed is 14.3 in frames per second(FPS). The results on the MS COCO dataset show that the proposed network outperforms FPN and Mask R-CNN at the cost of 5% accuracy loss and achieves a better balance between accuracy and efficiency.

Key words: object detection; feature pyramid; adaptive feature selection; lightweight network

CLC Number:

TP391

YANG Ai-ping， SONG Shang-yang， CHENG Si-meng. Lightweight Adaptive Feature Selection Network for Object Detection[J]. Journal of Northeastern University(Natural Science), 2021, 42(9): 1238-1245.

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