A lightweight detection algorithm for Camellia oleifera buds, stamens, and flowers using you only look once with large selective Kernel Network

To address the issues of occlusion and missed detections in small targets during the recognition of Camellia oleifera buds, stamens, and flowers and to improve recognition accuracy and computation speed, this study proposes a lightweight detection model based on YOLOv8s (You Only Look Once version 8 small). Firstly, we enhanced detection effectiveness by replacing the original YOLOv8s backbone with the Large Selective Kernel Network (LSKNet) and introducing the Minimum Point Distance Intersection over Union (MPD-IoU) loss function to accelerate convergence and improve recognition of overlapping targets. Secondly, for model lightweighting, we incorporated the Partial Convolution (PCConv) module to reduce parameters and floating-point operations (FLOPs) while enhancing feature representation. An additional detection head was added to improve small bud target detection. Experimental results show our improved model increased precision (P), recall (R), and mean average precision (mAP) by 0.3%, 1.2%, and 1.2% respectively over baseline YOLOv8s. Compared to mainstream models – YOLOv3-tiny, ScaledYOLOv4, YOLOv5s, YOLOv7, YOLOv8s, and Faster Region-Based Convolutional Neural Network (Faster R-CNN) – our model achieved mAP improvements of 1.8%, 1.5%, 1.4%, 2.1%, 1.2%, and 7.7% respectively. The optimized model demonstrates faster, more accurate identification of Camellia oleifera buds, stamens, and flowers, making it suitable for mobile deployment.