Aug 2026· 2026 IEEE International Conference on Mechatronics and Automation (ICMA)· pp. 365-370· 0 citations· 13 references
Abstract
Visual sorting robots often struggle with conventional horizontal bounding boxes when detecting irregularly placed solid waste. These boxes exhibit excessive background noise, making it difficult to extract precise depth data for 3D pose estimation. To address this issue, we developed MCB-Oriented RCNN, a more efficient detector for rotated objects based on the Oriented-RCNN framework. We replaced the resource-intensive ResNet-50 backbone with MobileNet-V3 to increase inference speed and integrated BiFPN and Coordinate Attention to maintain localization accuracy. Since lightweight models often suffer from accuracy losses, a teacher-student distillation strategy was applied to restore performance. Our experiments on a custom waste dataset show that the model achieves an mAP of 97.16% at 43.0 FPS. This corresponds to a 74.8% speed increase over the baseline model with a negligible accuracy loss of only 0.74%. These rotated boxes provide a direct approach to computing the 6-DoF coordinates required for robotic grasping.
A two-stage grasping framework that combines instance-level segmentation, planar grasp planning and time-compensated dynamic grasp modeling, providing an efficient, scalable and industrially applicable solution for automated metal waste sorting is presented.
Results indicate GOPD-YOLO’s potential as a lightweight vision-based solution for scallion posture recognition in automated harvesting and post-harvest processing as well as its potential as a lightweight vision-based solution for lightweight oriented object-detection.
Ya-Jing Jin, Ke-Jia Zhai, Xue Li et al.· Agriculture· 0 citations
This paper presents SHIFT (Surface-aware High-speed Integration For TSDFs), an efficient mapping framework designed to reduce this per-frame update cost of ESDFs by exploiting structural redundancy directly from 3D depth geometry.
To address the problems of low accuracy and slow speed in the recognition and positioning of composite material tee fittings in industrial environments, this paper proposes the YOLOv8-CCS algorithm. The algorithm designs a CGF module to enhance local and contextual feature extraction, introduces a CPS attention mechani...
Hai Yang, Yi-Sheng Zhao, Jia-Zhong Xu et al.· Proceedings of the Instituti...· 0 citations
This work presents a training-free suction grasping system for sorting deformed aseptic beverage cartons, decoupling target identification from grasp-point selection, and compares three geometric methods: k-nearest-neighbour PCA, Sobel cross-product, and RANSAC plane fitting.
Accurate localization of bounding boxes is a prerequisite for enabling vision-driven precision agriculture pipelines, as downstream tasks such as morphological feature extraction, growth monitoring, and digital-twin synchronization depend directly on the geometric quality of the detected boxes. Distance-IoU (DIoU) and...
Sonay Duman, Furkan Gözükara, Z. Yetgín et al.· Applied Sciences· 0 citations
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