摘要： 基于深度学习的目标检测算法因其模型复杂度和对计算能力的要求，难以部署在移动设备等低算力平台上。为了降低模型的规模，提出一种轻量级目标检测算法。该算法在自顶向下的特征融合的基础之上，通过添加注意力机制构建特征金字塔网络，以达到更细粒度的特征表达能力。该模型以分辨率为320×320的图像作为输入，浮点运算量只有0.72 B，并在VOC数据集上取得了74.2%的mAP，达到了与传统单阶段目标检测算法相似的精度。实验数据表明，该算法在保持了检测精度的同时显著降低了模型运算量，更适合低算力条件下的目标检测。

中图分类号： TN98；TP391
文献标识码： A
DOI：10.16157/j.issn.0258-7998.211320
中文引用格式： 赵义飞，王勇. 基于注意力特征金字塔的轻量级目标检测算法[J].电子技术应用，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

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