Research on Vehicle Target Detection Method Based on Improved YOLOv8
Source abstract
Abstract
To improve the performance of vehicle target detection in complex traffic environments and solve the problem that it is difficult to make a lightweight detection model, this paper proposes a lightweight vehicle detection model based on enhanced You Only Look Once v8. This method improves the feature extraction aggregation network by introducing an Adaptive Downsampling module, which can dynamically adjust the downsampling method, thereby increasing the model’s attention to key features, especially for small objects and occluded objects, while maintaining a lightweight structure, effectively reducing the model complexity while improving detection accuracy. A Lightweight Shared Convolution Detection Head was designed. By designing a shared convolution layer through group normalization, the detection head of the original model was improved, which can reduce redundant calculations and parameters and enhance the ability of global information fusion between feature maps, thereby achieving the purpose of improving computational efficiency. When tested in the KITTI 2D and UA-DETRAC datasets, the mAP of the proposed model was improved by 1.1% and 2.0%, respectively, the FPS was improved by 12% and 11%, respectively, the number of parameters was reduced by 33%, and the FLOPs were reduced by 28%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Advanced Data and IoT Technologies · Brain Tumor Detection and Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
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