HIGHLIGHTS
- who: TYPE and colleagues from the University of Adelaide, Australia have published the research work: An ef cient tomato-detection method based on improved YOLOv4-tiny model in complex environment, in the Journal: (JOURNAL)
- what: To solve this issue, the authors propose a new model based on YOLOv4-tiny. In this study, three major modifications were studied before obtaining the final result. The model showed a divergence under different occlusion conditions.
- how: Xu et_al proposed a fast method of detecting tomatoes in a complex scene for picking robots and their experimental results showed . . .
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