Hao Peng's research while affiliated with Hunan Agricultural University and other places
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Publications (4)
Currently, few deep models are applied to pepper-picking detection, and existing generalized neural networks face issues such as large model parameters, prolonged training times, and low accuracy. To address these challenges, this paper proposes the YOLO-chili target detection algorithm for chili pepper detection. Initially, the classical target de...
Based on the current research on the wine grape variety recognition task, it has been found that traditional deep learning models relying only on a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is a high degree of similarity between varieties. In order to effectively distinguish these simil...
Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the c...
Deep learning methodologies employed for biomass prediction often neglect the intricate relationships between labels and samples, resulting in suboptimal predictive performance. This paper introduces an advanced supervised contrastive learning technique, termed Improved Supervised Contrastive Deep Regression (SCDR), which is adept at effectively ca...
Citations
... Although the above detectors show significant improvements, the dependence on region proposals increases the computational overhead, prompting research into region proposal-free detectors. A major innovation occurred with the advent of YOLO [19,32,33]. YOLO divides an image into a grid and predicts bounding boxes using the class score of each cell to achieve unprecedented speed and accuracy. ...
