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Li Y, Rao Y, Jin X, Jiang Z, Wang Y, Wang T, Wang F, Luo Q, Liu L. YOLOv5s-FP: A Novel Method for In-Field Pear Detection Using a Transformer Encoder and Multi-Scale Collaboration Perception. Sensors (Basel) 2022; 23:s23010030. [PMID: 36616628 PMCID: PMC9823628 DOI: 10.3390/s23010030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/10/2022] [Accepted: 12/13/2022] [Indexed: 05/27/2023]
Abstract
Precise pear detection and recognition is an essential step toward modernizing orchard management. However, due to the ubiquitous occlusion in orchards and various locations of image acquisition, the pears in the acquired images may be quite small and occluded, causing high false detection and object loss rate. In this paper, a multi-scale collaborative perception network YOLOv5s-FP (Fusion and Perception) was proposed for pear detection, which coupled local and global features. Specifically, a pear dataset with a high proportion of small and occluded pears was proposed, comprising 3680 images acquired with cameras mounted on a ground tripod and a UAV platform. The cross-stage partial (CSP) module was optimized to extract global features through a transformer encoder, which was then fused with local features by an attentional feature fusion mechanism. Subsequently, a modified path aggregation network oriented to collaboration perception of multi-scale features was proposed by incorporating a transformer encoder, the optimized CSP, and new skip connections. The quantitative results of utilizing the YOLOv5s-FP for pear detection were compared with other typical object detection networks of the YOLO series, recording the highest average precision of 96.12% with less detection time and computational cost. In qualitative experiments, the proposed network achieved superior visual performance with stronger robustness to the changes in occlusion and illumination conditions, particularly providing the ability to detect pears with different sizes in highly dense, overlapping environments and non-normal illumination areas. Therefore, the proposed YOLOv5s-FP network was practicable for detecting in-field pears in a real-time and accurate way, which could be an advantageous component of the technology for monitoring pear growth status and implementing automated harvesting in unmanned orchards.
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Affiliation(s)
- Yipu Li
- College of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei 230036, China
- Anhui Provincial Key Laboratory of Smart Agricultural Technology and Equipment, Hefei 230036, China
| | - Yuan Rao
- College of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei 230036, China
- Anhui Provincial Key Laboratory of Smart Agricultural Technology and Equipment, Hefei 230036, China
| | - Xiu Jin
- College of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei 230036, China
- Anhui Provincial Key Laboratory of Smart Agricultural Technology and Equipment, Hefei 230036, China
| | - Zhaohui Jiang
- College of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei 230036, China
- Anhui Provincial Key Laboratory of Smart Agricultural Technology and Equipment, Hefei 230036, China
| | - Yuwei Wang
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei 230036, China
- Anhui Provincial Key Laboratory of Smart Agricultural Technology and Equipment, Hefei 230036, China
| | - Tan Wang
- College of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei 230036, China
- Anhui Provincial Key Laboratory of Smart Agricultural Technology and Equipment, Hefei 230036, China
| | - Fengyi Wang
- College of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
- Anhui Provincial Key Laboratory of Smart Agricultural Technology and Equipment, Hefei 230036, China
| | - Qing Luo
- College of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
- Anhui Provincial Key Laboratory of Smart Agricultural Technology and Equipment, Hefei 230036, China
| | - Lu Liu
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei 230036, China
- Anhui Provincial Key Laboratory of Smart Agricultural Technology and Equipment, Hefei 230036, China
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