[PDF] Improved Feature Fusion in YOLOv5 for Accurate Detection and Counting of Chinese Flowering Cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee) Buds | Semantic Scholar (2024)

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This paper presents a novel dynamic head framework to unify object detection heads with attentions by coherently combining multiple self-attention mechanisms between feature levels for scale- awareness, among spatial locations for spatial-awareness, and within output channels for task-awareness that significantly improves the representation ability of object detection Heads without any computational overhead.

The proposed model achieved the most advanced performance on data sets from Visual Object Classes 2012, the French Institute for Research in Computer Science and Automation, and the Swiss Federal Institute of Technology in Zurich and obtained the most competitive results in a triple-width VOC 2012 experiment carefully designed by the present study.

This paper systematically summarizes the research work on target recognition techniques for picking robots in recent years, analyzes the technical characteristics of different approaches, and concludes their development history.

This work proposes a novel dynamic sparse attention via bi-level routing to enable a more flexible allocation of computations with content awareness and presents a new general vision transformer, named BiFormer, which enjoys both good performance and high computational efficiency, especially in dense prediction tasks.

This work aims to provide a reference for future research on object detection and recognition techniques for fruit and vegetable harvesting robots based on digital image processing and traditional machine learning.

A deep-learning-based approach for efficiently estimating tea yield by counting tea buds in the field using an enhanced YOLOv5 model with the Squeeze and Excitation Network and combines the Hungarian matching and Kalman filtering algorithms to achieve accurate and reliable tea bud counting.

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