计算机科学
最小边界框
人工智能
交叉口(航空)
棱锥(几何)
聚类分析
特征工程
计算机视觉
特征(语言学)
模式识别(心理学)
深度学习
实时计算
工程类
图像(数学)
语言学
哲学
物理
光学
航空航天工程
作者
Hairong Zhang,Dongsheng Xu,Dayu Cheng,Xiaoliang Meng,Geng Xu,Wei Liu,Teng Wang
标识
DOI:10.1109/jstars.2023.3249216
摘要
Engineering vehicle recognition based on video surveillance is one of the key technologies to assist illegal land use monitoring. At present, the engineering vehicle recognition mainly adopts the traditional deep learning model with a large number of floating-point operations. So, it cannot be achieved in edge devices with limited computing power and storage in real-time. In addition, some lightweight models have problems with inaccurate bounding box locating, low recognition rate, and unreasonable selection of positive training samples for the small object. To solve the problems, the paper proposes an improved lightweight Yolo-Fastest V2 for engineering vehicle recognition fusing location enhancement and adaptive label assignment. The location-enhanced Feature Pyramid Network (FPN) structure combines deep and shallow feature maps to accurately localize bounding boxes. The grouping k-means clustering strategy and adaptive label assignment algorithm select an appropriate anchor for each object based on its shape and Intersection over Union (IoU). The study was conducted on Raspberry Pi 4B 2018 using two datasets and different models. Experiments show that our method achieves the optimal combination in speed and accuracy. Specifically, the mAP50 is increased by 7.02 % with the speed of 11.24FPS under the engineering vehicle data obtained by video surveillance in a rural area of China.
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