Work

YOLO-CDD: Real-time and accurate detection of latex collection bowl and flow states with enhanced multi-scale feature fusion

Year: 2025

Type: article

Abstract: Natural rubber is an essential industrial material extensively utilized in aerospace, transportation, and healthcare. Nonetheless, latex harvesting continues to depend significantly on manual assessment of collection bowl statuses (full, not full, tipped over) and flow trail conditions (exhausted, not exhausted), leading to inefficiency and elevated labor expenses—thus falling short of the requirements of contemporary precision agriculture. To effectively identify latex collection bowls and flow states in natural rubber harvesting, deep learning offers a scalable and intelligent solution for real-time monitoring. This paper presents an improved object detection model based on YOLOv12n, named YOLO with C2f-DSC and DWConv enhancements (YOLO-CDD), specifically designed for high-precision and real-time detection of latex collection bowls and latex flow states. An important innovation is the implementation of a self-designed Cross-Stage Partial Fusion with Depthwise Separable Convolution (C2f-DSC), which integrates multi-scale depthwise separable convolutions with an augmented C2f architecture to enhance multi-scale feature extraction and representation. This improves the model's capacity to identify objects at different scales. Furthermore, lightweight Depthwise Separable Convolution (DWConv) is incorporated into both the backbone and head network to diminish computational expense and parameter quantity without sacrificing accuracy, thereby enhancing the model's applicability for real-world implementation. A specialized dataset comprising latex collection bowls and flow trails was created to establish a robust foundation for this research. Experimental findings on the dataset indicate that YOLO-CDD attains a mAP0.50 of 80.7%, exceeding the baseline YOLOv12n by 3.6%, and enhancing mAP0.50:0.95 by 3.7%. Moreover, inference time and GFLOPs are diminished by 13.3% and 3.2%, respectively. In comparison to other advanced models like YOLOv10n and YOLOv11n, YOLO-CDD also exhibits significant enhancements across essential metrics. The YOLO-CDD model enhances detection accuracy while preserving high computational efficiency, rendering it ideal for real-time monitoring tasks.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.101305