YOLO-LF: a lightweight multi-scale feature fusion algorithm for wheat spike detection

Abstract

Wheat is one of the most significant crops in China, as its yield directly affects the country’s food security. Due to its dense, overlapping, and relatively fuzzy distribution, wheat spikes are prone to being missed in practical detection. Existing object detection models suffer from large model size, high computational complexity, and long computation times. Consequently, this study proposes a lightweight real-time wheat spike detection model called YOLO-LF. Initially, a lightweight backbone network is improved to reduce the model size and lower the number of parameters, thereby improving the runtime speed. Second, the structure of the neck is redesigned in the context of the wheat spike dataset to enhance the feature extraction capability of the network for wheat spikes and to achieve lightweightness. Finally, a lightweight detection head was designed to significantly reduce the FLOPs of the model and achieve further lightweighting. Experimental results on the test set indicate that the size of our model is 1.7 MB, the number of parameters is 0.76 M, and the FLOPs are 2.9, which represent reductions of 73, 74, and 64% compared to YOLOv8n, respectively. Our model demonstrates a latency of 8.6 ms and an FPS of 115 on Titan X, whereas YOLOv8n has a latency of 10.2 ms and an FPS of 97 on the same hardware. In contrast, our model is more lightweight and faster to detect, while the mAP@0.5 only decreases by 0.9%, outperforming YOLOv8 and other mainstream detection networks in overall performance. Consequently, our model can be deployed on mobile devices to provide effective assistance in the real-time detection of wheat spikes.