IoT-Based Precision Litchi Tracking and Counting Method Using Gated Metrics

Accurate and efficient multi-object tracking and counting methods are designed to address the challenges of counting in complex environments.This study presents a novel tracking and counting method called LitchiCount, integrating the multi-object tracking detection model LitchiDet with a counting module to address issues such as missing counts, repeated counts, and the lack of interpretability commonly found in traditional machine learning approaches. The method is designed with the guidance of the visual interpretable method Grad-CAM++, as well as the experimental validation method based on important features. To improve the detection accuracy of small targets under dense occlusion and overlapping, we proposed LitchiDet, which combines a small target detection layer, a decoupled fully connected attention with C3Ghost module (DFC-C3Ghost) and an efficient layer aggregation network block (ELANB). Our counting module improves target tracking accuracy and robustness in dense occlusion scenes while reducing counting errors from scene changes. We propose a Distance-generalized Intersection over Union association metric using a gating mechanism(DG-GM) and an AreaC counting strategy tailored to field intricate scenes. Finally, to enhance IoT deployment, we migrated LitchiCount to the Jetson AGX Xavier platform and optimized the model with TensorRT, significantly improving computational efficiency and real-time performance, particularly in resource-limited IoT environments, meeting real-time and low-power demands. The results demonstrated that our proposed method outperforms state-of-the-art detection models, as well as DeepSort-based counting methods in detection and counting. Importantly, by applying our method to the scenario of detecting and counting litchi from multiple perspectives in a field setting, we achieved low-repetitive and reliable counting, demonstrating the robust performance of this approach in real-world applications.