Hybrid Cross-Feature Interaction Attention Module for Object Detection in Intelligent Mobile Scenes

Tian, Di; Han, Yi; Liu, Yongtao; Li, Jiabo; Zhang, Ping; Liu, Ming

doi:10.3390/rs15204991

Cited by 2 publications

(1 citation statement)

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“…Object detection aims to locate and classify objects within images. Significant advancements in deep learning have established a robust foundation for its application in diverse fields such as intelligent driving vehicles [21], medical healthcare [22], agricultural robots [23], and remote sensing [24][25][26]. Currently, object detection methods are mainly categorized into two-stage and one-stage methods.…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Visual Perception for Object Detection and Semantic Segmentation in Intelligent Driving

Zhan,

Liu,

et al. 2024

Remote Sensing

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With the rapid development of intelligent driving vehicles, multi-task visual perception based on deep learning emerges as a key technological pathway toward safe vehicle navigation in real traffic scenarios. However, due to the high-precision and high-efficiency requirements of intelligent driving vehicles in practical driving environments, multi-task visual perception remains a challenging task. Existing methods typically adopt effective multi-task learning networks to concurrently handle multiple tasks. Despite the fact that they obtain remarkable achievements, better performance can be achieved through tackling existing problems like underutilized high-resolution features and underexploited non-local contextual dependencies. In this work, we propose YOLOPv3, an efficient anchor-based multi-task visual perception network capable of handling traffic object detection, drivable area segmentation, and lane detection simultaneously. Compared to prior works, we make essential improvements. On the one hand, we propose architecture enhancements that can utilize multi-scale high-resolution features and non-local contextual dependencies for improving network performance. On the other hand, we propose optimization improvements aiming at enhancing network training, enabling our YOLOPv3 to achieve optimal performance via straightforward end-to-end training. The experimental results on the BDD100K dataset demonstrate that YOLOPv3 sets a new state of the art (SOTA): 96.9% recall and 84.3% mAP50 in traffic object detection, 93.2% mIoU in drivable area segmentation, and 88.3% accuracy and 28.0% IoU in lane detection. In addition, YOLOPv3 maintains competitive inference speed against the lightweight YOLOP. Thus, YOLOPv3 stands as a robust solution for handling multi-task visual perception problems. The code and trained models have been released on GitHub.

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