LGB-YOLOv11n: A Position-Specific Lightweight Detector for Shiitake Appearance Pre-Grading on Edge Devices
TL;DR
A favorable accuracy–complexity trade-off is demonstrated and real-time model inference is supported for edge-based shiitake appearance pre-grading and support real-time model inference for edge-based shiitake appearance pre-grading.
Abstract
Automated shiitake pre-grading in forest-understory production environments is challenged by non-uniform illumination, cluttered backgrounds, occlusion, fine-grained appearance differences, and limited edge-computing resources. This study proposes LGB-YOLOv11n, a lightweight detector that assigns modules to feature levels according to their processing roles. Large-kernel separable attention is placed at the P4/16 backbone level and P3/8 detection branch to strengthen contour, texture, edge, and local-deformation representation. Ghost modules compress the P4/16 and P5/32 detection branches, while BiFPN-inspired weighted fusion replaces four neck fusion nodes. On a held-out test set, the model achieved 97.51% mAP@0.5 and 86.62% mAP@0.5:0.95, improvements of 1.21 and 5.62 percentage points over YOLOv11n, respectively. It used 2.39 M parameters, 7.72% fewer than the baseline. Gains were retained in all three leave-one-acquisition-date-out folds, and three random-seed runs yielded 86.49% ± 0.09% mAP@0.5:0.95. Cross-domain single-class evaluation on 222 external images yielded 89.52% AP@0.5 and 63.71% AP@0.5:0.95, compared with 86.73% and 59.84% for YOLOv11n. On an NVIDIA Jetson Orin NX with TensorRT FP16, LGB-YOLOv11n achieved 134.3 ± 6.73 FPS with a mean inference pipeline latency of 7.47 ± 0.39 ms. These results demonstrate a favorable accuracy–complexity trade-off and support real-time model inference for edge-based shiitake appearance pre-grading.