Introduction: With continuously increasing labor costs, an urgent need for automated apple- Qpicking equipment has emerged in the agricultural sector. Prior to apple harvesting, it is imperative that the equipment not only accurately locates the apples, but also discerns the graspability of the fruit. While numerous studies on apple detection have been conducted, the challenges related to determining apple graspability remain unresolved.
Methods: This study introduces a method for detecting multi-occluded apples based on an enhanced YOLOv5s model, with the aim of identifying the type of apple occlusion in complex orchard environments and determining apple graspability. Using bootstrap your own atent(BYOL) and knowledge transfer(KT) strategies, we effectively enhance the classification accuracy for multi-occluded apples while reducing data production costs. A selective kernel (SK) module is also incorporated, enabling the network model to more precisely identify various apple occlusion types. To evaluate the performance of our network model, we define three key metrics: AP, AP, and AP, representing the average detection accuracy for graspable, temporarily ungraspable, and ungraspable apples, respectively.
Results: Experimental results indicate that the improved YOLOv5s model performs exceptionally well, achieving detection accuracies of 94.78%, 93.86%, and 94.98% for AP, AP, and AP, respectively.
Discussion: Compared to current lightweight network models such as YOLOX-s and YOLOv7s, our proposed method demonstrates significant advantages across multiple evaluation metrics. In future research, we intend to integrate fruit posture and occlusion detection to f]urther enhance the visual perception capabilities of apple-picking equipment.
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http://dx.doi.org/10.3389/fpls.2023.1323453 | DOI Listing |
Curr Med Imaging
January 2025
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Background: Early and timely detection of pulmonary nodules and initiation treatment can substantially improve the survival rate of lung carcinoma. However, current detection methods based on convolutional neural networks (CNNs) cannot easily detect pulmonary nodules owing to low detection accuracy and the difficulty in detecting small-sized pulmonary nodules; meanwhile, more accurate CNN-based models are slow and require high hardware specifications.
Objective: The aim of this study is to develop a detection model that achieves both high accuracy and real-time performance, ensuring effective and timely results.
Sci Rep
December 2024
Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.
The intelligent identification of wear particles in ferrography is a critical bottleneck that hampers the development and widespread adoption of ferrography technology. To address challenges such as false detection, missed detection of small wear particles, difficulty in distinguishing overlapping and similar abrasions, and handling complex image backgrounds, this paper proposes an algorithm called TCBGY-Net for detecting wear particles in ferrography images. The proposed TCBGY-Net uses YOLOv5s as the backbone network, which is enhanced with several advanced modules to improve detection performance.
View Article and Find Full Text PDF3D Print Addit Manuf
December 2024
Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education, Shantou, China.
Cutting tools with orderly arranged diamond grits using additive manufacturing show better sharpness and longer service life than traditional diamond tools. A retractable needle jig with vacuum negative pressure was used to absorb and place grits in an orderly arranged manner. However, needle hole wear after a long service time could not promise complete grit adsorption forever.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Physical Education and Research, Heilongjiang University, Harbin, 150080, China.
Being able to quickly and accurately detect and recognize target balls is a key task for basketball robots in automated and intelligent sports competitions. This study aims to propose an effective target detection method for basketball robots, which is based on the improved YOLOv5s model and introduces spatial pyramid pooling and instance-batch normalization structure. The study first pre-trained the model and compared the migration training with the random initialization approach.
View Article and Find Full Text PDFPLoS One
December 2024
School of Physics and Electronic Information, Guangxi Minzu University, Nanning, China.
To achieve automated harvesting of hydroponic Chinese flowering cabbage, the detection and localization of the cabbage are crucial. This study proposes a two stages detection and localization algorithm for hydroponic Chinese flowering cabbage, which includes macro-detection and micro-localization. The macro-detection algorithm is named P-YOLOv5s-GRNF.
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