Lightweight Fisheye Object Detection Network with Transformer-Based Feature Enhancement for Autonomous Driving

Fisheye cameras, offering a wide field of view (FOV) of 360◦, are extensively employed for surround-view perception in autonomous driving. Compared with the object detection on the standard images, it lacks studies for fisheye images. Moreover, efficient perception is crucial for autonomous vehicles with limited computational capability. In this work, we introduce a lightweight fisheye object detection network with transformer-based feature enhancement for autonomous driving. Specifically, we leverage ShuffleNet V2 as a feature ex- traction network to reduce computation complexity and develop a transformer-based feature enhancement module (TFEM) to integrate multi-level features. Notably, we observe that data augmentation methods like mix-up and mosaic, effective on standard images, do not yield positive results on fisheye images. The results on the WoodScape dataset demonstrate that our method can achieve better performance with fewer parameters and floating-point operations per second (FLOPs). Extending our evaluation to the Microsoft Common Objects in Context (MS COCO) dataset shows that the proposed method has excellent generalization capability.