Lightweight YOLOv11n-Based Detection and Counting of Early-Stage Cabbage Seedlings from UAV RGB Imagery

This study proposes a lightweight adaptive neural network framework based on an improved YOLOv11n model to address the core challenges in identifying cabbage seedlings in visible light images captured by UAVs. These challenges include the loss of small-target features, poor adaptability to complex lighting conditions, and the low deployment efficiency of edge devices. First, the adaptive dual-path downsampling module (ADown) integrates average pooling and maximum pooling into a dual-branch structure to enhance background texture and crop edge features in a synergistic manner. Secondly, the Illumination Robust Contrast Learning Head (IRCLHead) utilizes a temperature-adaptive network to adjust the contrast loss function parameters dynamically. Combined with a dual-output supervision mechanism that integrates growth stage prediction and interference-resistant feature embedding, this module enhances the model’s robustness in complex lighting scenarios. Finally, a lightweight spatial-channel attention convolution module (LAConv) has been developed to optimize the model’s computational load by using multi-scale feature extraction paths and depth decomposition structures. Experiments demonstrate that the proposed architecture achieves an mAP@0.5 of 99.0% in detecting cabbage seedling growth cycles, improving upon the baseline model by 0.71 percentage points. Furthermore, it achieves an mAP@0.5:0.95 of 2.4 percentage points, reduces computational complexity (GFLOPs) by 12.7%, and drastically reduces inference time from 3.7 ms to 1.0 ms. Additionally, the model parameters are simplified by 3%. This model provides an efficient solution for the real-time counting of cabbage seedlings and lightweight operations in drone-based precision agriculture.