Research on traditional and deep learning strategies based on optical flow estimation - a review

Wang, Yifan; Wang, Wu; Li, Yang; Guo, Jinshi; Xu, Yu; Ma, Jiaqi; Ling, Yu; Fu, Yanan; Jia, Yaodong

doi:10.1016/j.jksuci.2024.102029

Cited by 1 publication

(1 citation statement)

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“…In contrast, the rise of deep learning provides strong support for image recognition technology and target detection, especially in the fields of intelligent driving and machine learning. That is, the target detection algorithm based on deep learning is more suitable for vehicle detection scenarios and has become the mainstream algorithm in this field [ 7 , 8 , 9 , 10 ]. The vehicle detection algorithm mainly focuses on the two key subtasks of vehicle location and classification and focuses on improving the accuracy of detection and the diversity of classification.…”

Section: Introductionmentioning

confidence: 99%

DV3-IBi_YOLOv5s: A Lightweight Backbone Network and Multiscale Neck Network Vehicle Detection Algorithm

Wang,

Shi,

Zhao

et al. 2024

Sensors

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Vehicle detection is a research direction in the field of target detection and is widely used in intelligent transportation, automatic driving, urban planning, and other fields. To balance the high-speed advantage of lightweight networks and the high-precision advantage of multiscale networks, a vehicle detection algorithm based on a lightweight backbone network and a multiscale neck network is proposed. The mobile NetV3 lightweight network based on deep separable convolution is used as the backbone network to improve the speed of vehicle detection. The icbam attention mechanism module is used to strengthen the processing of the vehicle feature information detected by the backbone network to enrich the input information of the neck network. The bifpn and icbam attention mechanism modules are integrated into the neck network to improve the detection accuracy of vehicles of different sizes and categories. A vehicle detection experiment on the Ua-Detrac dataset verifies that the proposed algorithm can effectively balance vehicle detection accuracy and speed. The detection accuracy is 71.19%, the number of parameters is 3.8 MB, and the detection speed is 120.02 fps, which meets the actual requirements of the parameter quantity, detection speed, and accuracy of the vehicle detection algorithm embedded in the mobile device.

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