A Performance Increment Strategy for Semantic Segmentation of Low-Resolution Images from Damaged Roads - Toledo, Rafael S.; Oliveira, Cristiano S.; Oliveira, Vitor H. T.; Antonelo, Eric A.; Wangenheim, Aldo von (Anais da Brazilian Conference on Intelligent Systems (BRACIS); 2024: Anais da XXXIV Brazilian Conference on Intelligent Systems, 2024)
Autonomous driving needs good roads, but 85% of Brazilian roads have damages that deep learning models may not regard as most semantic segmentation datasets for autonomous driving are high-resolution images of well-maintained urban roads. A representative dataset for emerging countries consists of low-resolution images of poorly maintained roads and includes labels of damage classes; in this scenario, three challenges arise: objects with few pixels, objects with undefined shapes, and highly underrepresented classes. To tackle these challenges, this work proposes the Performance Increment Strategy for Semantic Segmentation (PISSS) as a methodology of 14 training experiments to boost performance. With PISSS, we reached state-of-the-art results of 79.8 and 68.8 mIoU on the Road Traversing Knowledge (RTK) and Technik Autonomer Systeme 500 (TAS500) test sets, respectively. Furthermore, we also offer an analysis of DeepLabV3+ pitfalls for small object segmentation (Code available on https://github.com/tldrafael/pisss ).