On-road object detection is an important part of driverless technology. The on-road object detection task requires both detection speed and accuracy. We propose an improved RepVGG-based anchor-free real-time object detection algorithm to meet these requirements. The RepVggmodule is improved by a reparameterization method, and an adaptive Fusion-Distribution Feature Pyramid Network(FDFPN) structure is proposed, based on which an anchor-free object detection head with fewer hyperparameters is constructed to balance accuracy and speed. Experiments on KITTI dataset show that the accuracy of this method can reach 80.01%, and the inference latency is only 5.9ms in deployment mode.
Improved RepVGG-based Anchor-free Algorithm for On-road Object Detection
08.10.2022
2952239 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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