Abstract
Object detection is one of the most challenging problems in the field of computer vision, the practicality of object detection requires accuracy and real-time. YOLOv3 is a good real-time object detection algorithm, but with insufficient recall rate and insufficient positioning accuracy. The Attention Mechanism in deep learning is similar to the attention mechanism of human vision, which is to focus attention on important points in many information, select key information, and ignore other unimportant information. In this paper, we integrate Convolutional Block Attention Module (CBAM) in YOLOv3 in order to improves the detection accuracy and keep real-time. Compared to a conventional YOLOv3, we experimentally show the effectiveness and accuracy of the proposed method on the PASCAL VOC and MS-COCO datasets.
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Acknowledgement
This research was supported by the National Natural Science Foundation of China (Nos. 61672203, 61976079 & U1836102) and Anhui Natural Science Funds for Distinguished Young Scholar (No. 170808J08).
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Ban, MY., Tian, WD., Zhao, ZQ. (2020). Real-Time Object Detection Based on Convolutional Block Attention Module. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_4
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