Efficient and Lightweight Automatic Wheat Counting Method with Observation-Centric SORT for Real-Time Unmanned Aerial Vehicle Surveillance

Abstract

The number of wheat ears per unit area is crucial for assessing wheat yield, but automated wheat ear counting still faces significant challenges due to factors like lighting, orientation, and density variations. Departing from most static image analysis methodologies, this study introduces Wheat-FasterYOLO, an efficient real-time model designed to detect, track, and count wheat ears in video sequences. This model uses FasterNet as its foundational feature extraction network, significantly reducing the model’s parameter count and improving the model’s inference speed. We also incorporate deformable convolutions and dynamic sparse attention into the feature extraction network to enhance its ability to capture wheat ear features while reducing the effects of intricate environmental conditions. To address information loss during up-sampling and strengthen the model’s capacity to extract wheat ear features across varying feature map scales, we integrate a path aggregation network (PAN) with the content-aware reassembly of features (CARAFE) up-sampling operator. Furthermore, the incorporation of the Kalman filter-based target-tracking algorithm, Observation-centric SORT (OC-SORT), enables real-time tracking and counting of wheat ears within expansive field settings. Experimental results demonstrate that Wheat-FasterYOLO achieves a mean average precision (mAP) score of 94.01% with a small memory usage of 2.87MB, surpassing popular detectors such as YOLOX and YOLOv7-Tiny. With the integration of OC-SORT, the composite higher order tracking accuracy (HOTA) and counting accuracy reached 60.52% and 91.88%, respectively, while maintaining a frame rate of 92 frames per second (FPS). This technology has promising applications in wheat ear counting tasks.

Improved YOLOv8 and SAHI Model for the Collaborative Detection of Small Targets at the Micro Scale: A Case Study of Pest Detection in Tea

“…In recent years, deep learning has found widespread applications across various sectors, particularly in agriculture [6][7][8]. Target detection using deep learning has gained prominence in computer vision research and is extensively employed in crop harvesting [9,10], pest and disease detection [11][12][13], yield prediction [14][15][16], unmanned farm monitoring [17,18], and other areas. Through the development of intricate parallel models, deep learning technology has effectively addressed challenges such as limited data resources, information integration difficulties, and the low efficiency of knowledge utilization in agricultural settings.…”

Light-FC-YOLO: A Lightweight Method for Flower Counting Based on Enhanced Feature Fusion with a New Efficient Detection Head

“…In combination with the Wise-IoU loss function [23], the model's ability to recognize occluded objects is enhanced. Jie Chen et al [24] used FasterNet [25] as the basic feature extraction network and designed a lightweight wheat counting model, Wheat-FasterYOLO, significantly reducing the model's parameter quantity. They introduced deformable convolution [26] and a dynamic sparse attention mechanism [27] in the network, enhancing the model's ability to extract wheat features and improving the accuracy of wheat ear counting.…”