Real-Time Image Semantic Segmentation Networks with Residual Depth-Wise Separable Blocks

Doan, Van-Viet; Nguyen, Duy-Hung; Tran, Quoc-Long; Nguyen, Do-Van; Le, Trung-Nghia

doi:10.1109/scis-isis.2018.00037

Cited by 5 publications

(1 citation statement)

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“…In [ 23 ], Aslan et al developed a deep learning algorithm for humanoid robots to walk to the target using semantic segmentation and a deep Q network. In [ 24 ], Doan et al proposed a semantic segmentation network with residual depth-wise separable blocks to detect street objects such as cars and pedestrians. In [ 25 ], Kowalewski et al presented the object-level semantic perception of the environment for indoor mobile robots.…”

Section: Related Workmentioning

confidence: 99%

Object-of-Interest Perception in a Reconfigurable Rolling-Crawling Robot

Semwal

Lee

Sánchez

et al. 2022

Sensors

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Cebrenus Rechenburgi, a member of the huntsman spider family have inspired researchers to adopt different locomotion modes in reconfigurable robotic development. Object-of-interest perception is crucial for such a robot to provide fundamental information on the traversed pathways and guide its locomotion mode transformation. Therefore, we present a object-of-interest perception in a reconfigurable rolling-crawling robot and identifying appropriate locomotion modes. We demonstrate it in Scorpio, our in-house developed robot with two locomotion modes: rolling and crawling. We train the locomotion mode recognition framework, named Pyramid Scene Parsing Network (PSPNet), with a self-collected dataset composed of two categories paths, unobstructed paths (e.g., floor) for rolling and obstructed paths (e.g., with person, railing, stairs, static objects and wall) for crawling, respectively. The efficiency of the proposed framework has been validated with evaluation metrics in offline and real-time field trial tests. The experiment results show that the trained model can achieve an mIOU score of 72.28 and 70.63 in offline and online testing, respectively for both environments. The proposed framework’s performance is compared with semantic framework (HRNet and Deeplabv3) where the proposed framework outperforms in terms of mIOU and speed. Furthermore, the experimental results has revealed that the robot’s maneuverability is stable, and the proposed framework can successfully determine the appropriate locomotion modes with enhanced accuracy during complex pathways.

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Section: Related Workmentioning

confidence: 99%