Real-Time Speed Bump Detection Using Image Segmentation for Autonomous Vehicles

ArunPriyan, J; Variyar, V. V. Sajith; Adarsh, S.

doi:10.1007/978-3-030-30465-2_35

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…There are two main categories for identifying speed bumps using CNNs: semantic segmentation-based and anchor box-based. Semantic segmentation-based methods include SegNet, first proposed by Vijay et al [22] and applied to speed bump identification work by Arunpriyan et al [23]. Varona et al [24] proposed a method based on CNNs, Long Short-Term Memory Networks (LSTMs), and Reservoir Computing Models.…”

Section: Speed Bump Detection Methodsmentioning

confidence: 99%

Research on Visual Perception of Speed Bumps for Intelligent Connected Vehicles Based on Lightweight FPNet

Wang,

Luo,

et al. 2024

Sensors

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In the field of intelligent connected vehicles, the precise and real-time identification of speed bumps is critically important for the safety of autonomous driving. To address the issue that existing visual perception algorithms struggle to simultaneously maintain identification accuracy and real-time performance amidst image distortion and complex environmental conditions, this study proposes an enhanced lightweight neural network framework, YOLOv5-FPNet. This framework strengthens perception capabilities in two key phases: feature extraction and loss constraint. Firstly, FPNet, based on FasterNet and Dynamic Snake Convolution, is developed to adaptively extract structural features of distorted speed bumps with accuracy. Subsequently, the C3-SFC module is proposed to augment the adaptability of the neck and head components to distorted features. Furthermore, the SimAM attention mechanism is embedded within the backbone to enhance the ability of key feature extraction. Finally, an adaptive loss function, Inner–WiseIoU, based on a dynamic non-monotonic focusing mechanism, is designed to improve the generalization and fitting ability of bounding boxes. Experimental evaluations on a custom speed bumps dataset demonstrate the superior performance of FPNet, with significant improvements in key metrics such as the mAP, mAP50_95, and FPS by 38.76%, 143.15%, and 51.23%, respectively, compared to conventional lightweight neural networks. Ablation studies confirm the effectiveness of the proposed improvements. This research provides a fast and accurate speed bump detection solution for autonomous vehicles, offering theoretical insights for obstacle recognition in intelligent vehicle systems.

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Section: Speed Bump Detection Methodsmentioning

confidence: 99%

Research on Visual Perception of Speed Bumps for Intelligent Connected Vehicles Based on Lightweight FPNet

Wang,

Luo,

et al. 2024

Sensors

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show abstract

“…Some speed bumps are clearly marked, while others are not, and running over them at speed can cause serious damage to vehicles. Arunpriyan et al [7] designed a deep learning-based image segmentation method to detect speed breakers so using only relevant images would allow the steering and braking algorithms to work more quickly. Varma et al [8] developed a deep learning-based speed bump detection system and collected the depth value of the speed breakers using the stereovision ZED depth camera.…”

Section: Introductionmentioning

confidence: 99%