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| Object detection based on shallow spatial feature fusion and adaptive channel selection |
| 1.Key Laboratory of Data Engineering and Visual Computing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; 2.College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China |
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Abstract To solve the problem of reduced detection speed due to deepening the backbone network, an object detection method was proposed based on feature fusion and channel selection. The information in the different subspace was reasonably multiplexed in each down-sampling stage, and the feature fusion was performed with the proposed 8-fold, 4-fold and 2-fold down-sampling modules. The fused feature maps were assembled into the SSD network after adaptive channel selection for strengthening the role of global information in the object detection model. A classification loss function based on cosine distance was designed to make the object classification more accurate. The VGG network was used as backbone network. Referring to SSD object detection network, the proposed down-sampling feature fusion module, the adaptive channel selection module and the improved loss function were also added to conduct the multiple groups of comparative experiments. The results show that the mean average precision of the proposed method for object detection reaches 82.2% on the datasets of Pascal VOC 2007 and Pascal VOC 2012 with the image input size of network of 512×512, which is better than that of the single-stage object detection models. The proposed method achieves the same performance as the detection models with deeper backbone network under the condition of ensuring real-time speed.
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Received: 13 October 2020
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2022, Vol. 43 
