中圖分類(lèi)號(hào)： TN98,；TP391 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.211320 中文引用格式： 趙義飛,，王勇. 基于注意力特征金字塔的輕量級(jí)目標(biāo)檢測(cè)算法[J].電子技術(shù)應(yīng)用，2021,，47(10)：33-37. 英文引用格式： Zhao Yifei,，Wang Yong. Lightweight object detection algorithm based on attention feature pyramid network[J]. Application of Electronic Technique，2021,，47(10)：33-37.
Lightweight object detection algorithm based on attention feature pyramid network
Zhao Yifei,，Wang Yong
Faculty of Information Technology，Beijing University of Technology,，Beijing 100124,，China
Abstract： Object detection algorithms based on deep learning are difficult to deploy on low computing power platforms such as mobile devices due to their complexity and computational demands. In order to reduce the scale of the model, this paper proposed a lightweight object detection algorithm. Based on the top-down feature fusion, the algorithm built a feature pyramid network by adding an attention mechanism to achieve more fine-grained feature expression capabilities. The proposed model took an image with a resolution of 320×320 as input and had only 0.72 B FLOPs, achieved 74.2% mAP on the VOC dataset and the accuracy is similar to traditional one-stage object detection algorithms. Experimental data shows that the algorithm significantly reduces the computational complexity of the model, maintains the accuracy, and is more suitable for object detection with low computing power.
Key words : object detection；feature pyramid,；attention mechanism,；lightweight algorithm
