Object detection based on RetinaNet+CBAM attention mechanism

Zhiwei Gao; Ningbo Zhang

doi:10.1117/12.2673531

22 May 2023 Object detection based on RetinaNet+CBAM attention mechanism

Zhiwei Gao, Ningbo Zhang

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126400R (2023) https://doi.org/10.1117/12.2673531
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

As the core technologies represented by Internet of Things (IoT) technology and computer vision technology booming, object detection algorithms based on deep learning have received high attention in various fields. Object detection is not only a core problem in the field of computer vision, but also a prerequisite and basis for many computer vision tasks, and has important applications in the fields of autonomous driving and video surveillance. The task of object detection is to find all the targets of interest in an image and determine their category and location. Object detection includes two tasks: object classification and object localization. However, object classification focuses on the most discriminative part of the feature map, and object localization requires a feature map focusing on the whole region of the object. Therefore, in order to improve the detection accuracy, we propose a CBAM-RetinaNet network model.CBAM (Convolutional Block Attention Module) is a lightweight convolutional attention module, which combines channel and spatial attention mechanism modules. The channel attention module focuses on the meaningful information in the input image, which would lead to an increase in the accuracy of object classification. The spatial attention module focuses on the location information of the object, which may improve the accuracy of object localization. The experiment results show that the proposed achieves better object detection performance on the PASCAL VOC2007 dataset, and the accuracy is 2% higher than that of the RetinaNet model, which has good experimental results.

Citation Download Citation

Zhiwei Gao and Ningbo Zhang "Object detection based on RetinaNet+CBAM attention mechanism", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126400R (22 May 2023); https://doi.org/10.1117/12.2673531

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KEYWORDS

Object detection

Feature extraction

Target detection

Deep learning

Image classification

Computer vision technology

Design and modelling

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