ArticleMicromachines2021
Object Detection Method for Grasping Robot Based on Improved YOLOv5.
Article in Micromachines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Automated detection of mycobacterium tuberculosis based on cloud computing.BMC infectious diseases · 2025Article
- [Deep overparameterized blood cell detection algorithm utilizing hybrid attention mechanisms].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Streaks on martian slopes are dry.Nature communications · 2025Article
- Research on Mine-Personnel Helmet Detection Based on Multi-Strategy-Improved YOLOv11.Sensors (Basel, Switzerland) · 2024Article
- An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN.Scientific reports · 2024Article
- Automatic deep learning detection of overhanging restorations in bitewing radiographs.Dento maxillo facial radiology · 2024Article
- Research on a Metal Surface Defect Detection Algorithm Based on DSL-YOLO.Sensors (Basel, Switzerland) · 2024Article
- A Global Dataset of Potential Chloride Deposits on Mars as Identified by TGO CaSSIS.Scientific data · 2024Article
- Combined MediaPipe and YOLOv5 range of motion assessment system for spinal diseases and frozen shoulder.Scientific reports · 2024Article
- Determining the Presence and Size of Shoulder Lesions in Sows Using Computer Vision.Animals : an open access journal from MDPI · 2023Article
- Enhanced batch sorting and rapid sensory analysis of Mackerel products using YOLOv5s algorithm and CBAM: Validation through TPA, colorimeter, and PLSR analysis.Food chemistry: X · 2023Article
- A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model.Micromachines · 2023Article
- Automatic Detection and Measurement of Renal Cysts in Ultrasound Images: A Deep Learning Approach.Healthcare (Basel, Switzerland) · 2023Article
- Equipment Identification and Localization Method Based on Improved YOLOv5s Model for Production Line.Sensors (Basel, Switzerland) · 2022Article
- Fast identification and quantification of c-Fos protein using you-only-look-once-v5.Frontiers in psychiatry · 2022Article
- Recognition of terminal buds of densely-planted Chinese fir seedlings using improved YOLOv5 by integrating attention mechanism.Frontiers in plant science · 2022Article
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Authors and funding
7 authors.
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Abstract
In the industrial field, the anthropomorphism of grasping robots is the trend of future development, however, the basic vision technology adopted by the grasping robot at this stage has problems such as inaccurate positioning and low recognition efficiency. Based on this practical problem, in order to achieve more accurate positioning and recognition of objects, an object detection method for grasping robot based on improved YOLOv5 was proposed in this paper. Firstly, the robot object detection platform was designed, and the wooden block image data set is being proposed. Secondly, the Eye-In-Hand calibration method was used to obtain the relative three-dimensional pose of the object. Then the network pruning method was used to optimize the YOLOv5 model from the two dimensions of network depth and network width. Finally, the hyper parameter optimization was carried out. The simulation results show that the improved YOLOv5 network proposed in this paper has better object detection performance. The specific performance is that the recognition precision, recall, mAP value and F1 score are 99.35%, 99.38%, 99.43% and 99.41% respectively. Compared with the original YOLOv5s, YOLOv5m and YOLOv5l models, the mAP of the YOLOv5_ours model has increased by 1.12%, 1.2% and 1.27%, respectively, and the scale of the model has been reduced by 10.71%, 70.93% and 86.84%, respectively. The object detection experiment has verified the feasibility of the method proposed in this paper.
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