YOLO-SCL: a lightweight detection model for citrus psyllid based on spatial channel interaction

Abstract

Efficient and accurate detection and providing early warning for citrus psyllids is crucial as they are the primary vector of citrus huanglongbing. In this study, we created a dataset comprising images of citrus psyllids in natural environments and proposed a lightweight detection model based on the spatial channel interaction. First, the YOLO-SCL model was based on the YOLOv5s architecture, which uses an efficient channel attention module to perform local channel attention on the inputs in the recursive gated convolutional modules to achieve a combination of global spatial and local channel interactions, improving the model's ability to express the features of the critical regions of small targets. Second, the lightweight design of the 21st layer C3 module in the neck network of the YOLO-SCL model and the small target feature information were retained to the maximum extent by deleting the two convolutional layers, whereas the number of parameters was reduced to improve the detection accuracy of the model. Third, with the detection accuracy of the YOLO-SCL model as the objective function, the black widow optimization algorithm was used to optimize the hyperparameters of the YOLO-SCL model, and the iterative mechanism of swarm intelligence was used to further improve the model performance. The experimental results showed that the YOLO-SCL model achieved a mAP@0.5 of 97.07% for citrus psyllids, which was 1.18% higher than that achieved using conventional YOLOv5s model. Meanwhile, the number of parameters and computation amount of the YOLO-SCL model are 6.92 M and 15.5 GFlops, respectively, which are 14.25% and 2.52% lower than those of the conventional YOLOv5s model. In addition, after using the black widow optimization algorithm to optimize the hyperparameters, the mAP@0.5 of the YOLO-SCL model for citrus psyllid improved to 97.18%, making it more suitable for the natural environments in which citrus psyllids are to be detected. The experimental results showed that the YOLO-SCL model has good detection accuracy for citrus psyllids, and the model was ported to the Jetson AGX Xavier edge computing platform, with an average processing time of 38.8 ms for a single-frame image and a power consumption of 16.85 W. This study provides a new technological solution for the safety of citrus production.

Keywords: YOLO; citrus psyllids; hyperparameter optimization; lightweight; recursive gated convolution; small target detection.