期刊
SENSORS
卷 21, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/s21093031
关键词
traffic scenes; object detection; multi-scale channel attention; attention feature fusion
资金
- National Natural Science Foundation of China [51775082, 61976039]
- China Fundamental Research Funds for the Central Universities [DUT19LAB36, DUT20GJ207]
- Science and Technology Innovation Fund of Dalian [2018J12GX061]
The study introduces a small object detection method in traffic scenes based on attention feature fusion, which utilizes multi-scale channel attention block and attention feature fusion block to improve the accuracy and performance of small object detection.
There are many small objects in traffic scenes, but due to their low resolution and limited information, their detection is still a challenge. Small object detection is very important for the understanding of traffic scene environments. To improve the detection accuracy of small objects in traffic scenes, we propose a small object detection method in traffic scenes based on attention feature fusion. First, a multi-scale channel attention block (MS-CAB) is designed, which uses local and global scales to aggregate the effective information of the feature maps. Based on this block, an attention feature fusion block (AFFB) is proposed, which can better integrate contextual information from different layers. Finally, the AFFB is used to replace the linear fusion module in the object detection network and obtain the final network structure. The experimental results show that, compared to the benchmark model YOLOv5s, this method has achieved a higher mean Average Precison (mAP) under the premise of ensuring real-time performance. It increases the mAP of all objects by 0.9 percentage points on the validation set of the traffic scene dataset BDD100K, and at the same time, increases the mAP of small objects by 3.5%.
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