YOLOv5s-FP: A Novel Method for In-Field Pear Detection Using a Transformer Encoder and Multi-Scale Collaboration Perception

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.

Intelligent Detection of Lightweight “Yuluxiang” Pear in Non-Structural Environment Based on YOLO-GEW

“…This lays a solid foundation for deployment on embedded devices and provides technical support for the development of the "Yuluxiang" pear-picking robot. In the field of pear fruit detection, Li et al [31] utilized a ground tripod and cameras mounted on a drone platform to capture high-resolution images of pears for monitoring their growth status. They proposed an advanced multi-scale collaborative perception network known as YOLOv5sFP specifically designed for accurate pear detection.…”

YOLOv5-ACS: Improved Model for Apple Detection and Positioning in Apple Forests in Complex Scenes

“…This algorithm uses a feature extraction network made up of concatenated homogeneous residual blocks to simplify the feature map scale of object detection. Li et al [31] proposed a multi-scale collaborative perception network called YOLOv5s-FP for detecting pears, which combines specific and holistic features to address issues such as occlusion in orchards and the diversity of image capture positions. The aforementioned advancements have facilitated the progress of tree fruit detection and can serve as a guide for detecting comparable fruits on trees.…”

Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm

“…The proposed model achieved an average accuracy of 68% in discriminating and classifying apple flowers with varying opening degrees. Li et al [11] enhanced the YOLOv5 algorithm's detection accuracy of pears by integrating a Transformer Encoder, demonstrating a maximum average accuracy of 96.12% and robustness improvement in different shading and lighting conditions. After YOLOv5, new algorithms such as YOLOv6 [12], YOLOv7 [13], YOLOv8 [14], YOLOX [15], and YOLOF [16] have been proposed in recent years.…”