LGD-YOLO: An Asymmetric Lightweight Network with Dynamic Feature Alignment for Greenhouse Tomato Maturity Detection
TL;DR
LGD-YOLO is proposed as an asymmetric lightweight network adapted from YOLOv10n specifically for edge-based tomato maturity detection, integrating a C2f-GMKSF module utilizing grouped multi-kernel convolutions to extract multi-scale textures with limited computational overhead.
Abstract
Greenhouse tomato detection faces critical challenges due to dense fruit occlusion, background interference, and the stringent computational constraints inherent to edge-deployed harvesting robots. Resolving these bottlenecks requires efficient architectures. We propose LGD-YOLO as an asymmetric lightweight network adapted from YOLOv10n specifically for edge-based tomato maturity detection. The architecture integrates a C2f-GMKSF module utilizing grouped multi-kernel convolutions to extract multi-scale textures with limited computational overhead. Precise feature alignment under occluded conditions is subsequently achieved through a Dy-HSFPN structure, accompanied by a C2f-CFCGLU module that expands the receptive field while preserving linear complexity. Furthermore, replacing the traditional detection head with a partial convolution head reduces memory access costs. A Focaler-Wise-SIoU loss function is utilized to stabilize bounding box regression against the lightweight penalty without introducing inference latency. Performance evaluations on a custom three-class dataset with a 180-image test set yield an 88.0% mAP@50. Relative to the baseline model, LGD-YOLO improves detection accuracy by 0.6 percentage points while shrinking the parameter volume by 37.6% to 1.41 M and lowering computational demand by 41.5% to 3.8 GFLOPs. Hardware deployment on an NVIDIA Jetson AGX Orin achieves a sustained processing speed of 40.6 FPS, while the weight file is 2.99 MB, supporting its feasibility for real-time agricultural robotics.