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A novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles.
Front Plant Sci
December 2024
Institute of Technology, Anhui Agricultural University, Hefei, China.
Introduction: The rapid urbanization of rural regions, along with an aging population, has resulted in a substantial manpower scarcity for agricultural output, necessitating the urgent development of highly intelligent and accurate agricultural equipment technologies.
Methods: This research introduces YOLOv8-PSS, an enhanced lightweight obstacle detection model, to increase the effectiveness and safety of unmanned agricultural robots in intricate field situations. This YOLOv8-based model incorporates a depth camera to precisely identify and locate impediments in the way of autonomous agricultural equipment.
Avoiding catastrophic overfitting in fast adversarial training with adaptive similarity step size.
PLoS One
January 2025
School of Electrical Engineering, Zhejiang University, Hangzhou, China.
Adversarial training has become a primary method for enhancing the robustness of deep learning models. In recent years, fast adversarial training methods have gained widespread attention due to their lower computational cost. However, since fast adversarial training uses single-step adversarial attacks instead of multi-step attacks, the generated adversarial examples lack diversity, making models prone to catastrophic overfitting and loss of robustness.
View Article and Find Full Text PDFDetection of Critical Parts of River Crab Based on Lightweight YOLOv7-SPSD.
Sensors (Basel)
November 2024
College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
The removal of back armor marks the first stage in the comprehensive processing of river crabs. However, the current low level of mechanization undermines the effectiveness of this process. By integrating robotic systems with image recognition technology, the efficient removal of dorsal armor from river crabs is anticipated.
View Article and Find Full Text PDFDefect Detection and 3D Reconstruction of Complex Urban Underground Pipeline Scenes for Sewer Robots.
Sensors (Basel)
November 2024
Hefei Intelligent Robot Institute, Hefei 230601, China.
Detecting defects in complex urban sewer scenes is crucial for urban underground structure health monitoring. However, most image-based sewer defect detection models are complex, have high resource consumption, and fail to provide detailed damage information. To increase defect detection efficiency, visualize pipelines, and enable deployment on edge devices, this paper proposes a computer vision-based robotic defect detection framework for sewers.
View Article and Find Full Text PDFDeep learning neural network-assisted badminton movement recognition and physical fitness training optimization.
Heliyon
October 2024
Graduate School, Metharath University, Bangkok, 10400, Thailand.
Article Synopsis
- This work addresses the issue of low accuracy in recognizing badminton movement trajectories by enhancing the visual system used in badminton robots.
- It employs a convolutional neural network with an attention mechanism to effectively track flying badminton in video streams, while improving the Tiny YOLOv2 detection network to better handle small targets.
- The results show high performance, with an average tracking accuracy of 91.40% and superior metrics in various scenarios compared to traditional algorithms, significantly aiding badminton movement recognition.
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