Naslov
Efficient Ladder-Style DenseNets for Semantic Segmentation of Large Images

Autori
Krešo, Ivan ; Krapac, Josip ; Šegvić, Siniša

Izvornik
Ieee transactions on intelligent transportation systems (1524-9050) 22 (2020), 8; 4951-4961

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
computer vision ; supervised learning ; image segmentation ; road transportation

Sažetak
Recent progress of deep image classification models has provided a great potential for improving related computer vision tasks. However, the transition to semantic segmentation is hampered by strict memory limitations of contemporary GPUs. The extent of feature map caching required by convolutional backprop poses significant challenges even for moderately sized Pascal images, while requiring careful architectural considerations when input resolution is in the megapixel range. To address these concerns, we propose a novel ladder-style DenseNet-based architecture which features high modelling power, efficient upsampling, and inherent spatial efficiency which we unlock with checkpointing. The resulting models deliver high performance and allow training at megapixel resolution on commodity hardware. The presented experimental results outperform the state-of-the-art in terms of prediction accuracy and execution speed on Cityscapes, VOC 2012, CamVid and ROB 2018 datasets. Source code at \url{; ; ; https://github.com/ivankreso/LDN}; ; ; .

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Tehnologija prometa i transport

POVEZANOST RADA