Small obstacles detection on roads scenes using semantic segmentation for the safe navigation of autonomous vehicles.

Currently, autonomous vehicles and intelligent robots are used in an extensive set of industrial applications. However, in the recent past, the frequency with which small obstacles arise on highways has risen dramatically, which will lead to severe incidents on highways. Hence, small obstacle detection is critical for increasing the efficiency of autonomous vehicles to enable their safe navigation to avoid road accidents. On the other hand, these small-size obstacles are of varying sizes, shapes, and colors and are difficult to be detected under low lighting and illumination conditions. Existing research studies suggest deep-learning-assisted semantic segmentation models; however, an optimal model along with improved performance is of greater necessity. From this perspective, we have suggested transfer learning-based approaches using state-of-the-art semantic segmentation models namely UNet++, PSPNet, PANNet, LinkNet, and DeepLabV3+ for the detection of small-size obstacles under strict lighting and illumination conditions. Furthermore, we used images of road scenes with extremely small-size obstacles, which were neglected by past research studies, since these obstacles were not included in the databases that they were using in their findings and may have failed to effectively address the problem of obstacle detection. Moreover, for faster and better convergence, we have modified the backbone architectures of these models with Residual-Network (ResNet)-18 and ResNet-34 trained on ImageNet weights. It is observed that DeepLabV3 + ResNet-18 as backbone architecture shows the highest results with a mean intersection-over-union score of 64% along with a 95% value of accuracy. (c) 2022 SPIE and IS&T