CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentation
作者：
Highlights：
• A large-scale dataset for 2D vehicle detection and scene understanding in self-driving cars in un-pattern rural, urban, and hilly road traffic scenarios.
• Scene segmentation benchmark – 7500 pixel-level annotated images including 44 classes grouped into nature, infrastructure, moving and static objects, signboards, and misc.
• Vehicle detection & recognition benchmark – 15,000 annotated images covering 25 classes with 50,348 labels.
• A comparative analysis of existing vehicle detection and scene segmentation dataset with our proposed benchmarks.
摘要
•A large-scale dataset for 2D vehicle detection and scene understanding in self-driving cars in un-pattern rural, urban, and hilly road traffic scenarios.•Scene segmentation benchmark – 7500 pixel-level annotated images including 44 classes grouped into nature, infrastructure, moving and static objects, signboards, and misc.•Vehicle detection & recognition benchmark – 15,000 annotated images covering 25 classes with 50,348 labels.•A comparative analysis of existing vehicle detection and scene segmentation dataset with our proposed benchmarks.
论文关键词：Self-driving cars,Machine vision,Vehicle detection,Vehicle recognition,Scene segmentation,Deep neural networks
论文评审过程：Received 8 June 2021, Revised 4 December 2021, Accepted 11 February 2022, Available online 17 February 2022, Version of Record 4 March 2022.
论文官网地址：https://doi.org/10.1016/j.image.2022.116667
