Work

Lightweight Detection of Broccoli Heads in Complex Field Environments Based on LBDC-YOLO

Year: 2024

Type: article

Abstract: Implementing selective harvesting of broccoli using robots in natural field environments necessitates a precise and efficient broccoli head detection model. However, challenges arise from factors like lighting, obstruction, shadows, and water droplets, which impact the accurate detection of broccoli heads. Additionally, hardware limitations on mobile robot platforms impose requirements on the computational complexity of the detection model.Therefore, this study introduces a lightweight and high-precision detection model named LBDC-YOLO (Lightweight Broccoli Detection in Complex Environment – You Look Only Once), based on the improved YOLOv8 (You Look Only Once, Version 8). The model incorporates the Slim-neck design paradigm based on GSConv to reduce computational complexity. Furthermore, Triplet attention is integrated into the backbone network to capture cross-dimensional interactions between spatial and channel dimensions, enhancing the model's feature extraction capability under multiple interfering factors. The original neck network structure is replaced with BiFPN, optimizing the cross-layer connection structure, and employing weighted fusion methods for better integration of multi-scale features.The model undergoes training and testing on a dataset collected in real field conditions, featuring broccoli images under various influencing factors. Experimental results demonstrate that LBDC-YOLO achieves an average detection accuracy of 94.44% for broccoli. Compared to the original YOLOv8n, LBDC-YOLO achieves a 32.1% reduction in computational complexity, a 47.8% decrease in parameters, a 44.4% reduction in model size, and a 0.47 percentage points accuracy improvement. When compared to models such as YOLOv5n, YOLOv5s, and YOLOv7-tiny, LBDC-YOLO exhibits higher detection accuracy and lower computational complexity, presenting clear advantages for deployment on mobile robot platforms and broccoli detection tasks in complex field environments. LBDC-YOLO stands as a viable solution for deploying on mobile robot platforms for broccoli head detection in complex field environments.

Source: Agronomy

DOI: 10.3390/agronomy14102359