Simultaneous vehicle and lane detection via MobileNetV3 in car following scene.

ABSTRACT: Aiming at vehicle and lane detections on road scene, this paper proposes a vehicle and lane line joint detection method suitable for car following scenes. This method uses the codec structure and multi-task ideas, shares the feature extraction network and feature enhancement and fusion module. Both ASPP (Atrous Spatial Pyramid Pooling) and FPN (Feature Pyramid Networks) are employed to improve the feature extraction ability and real-time of MobileNetV3, the attention mechanism CBAM (Convolutional Block Attention Module) is introduced into YOLOv4, an asymmetric network architecture of "more encoding-less decoding" is designed for semantic pixel-wise segmentation network. The proposed model employed improved MobileNetV3 as feature ex-traction block, and the YOLOv4-CBAM and Asymmetric SegNet

SUBMITTER: Deng T 

PROVIDER: S-EPMC8896667 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature