Research on improved YOLOx weed detection based on lightweight attention module

Abstract

Accurate weed detection is essential for precise weed control in farmland, and machine vision serves as an effective method for identifying and targeting these unwanted plants. An advanced YOLOx weed detection model, incorporating a deep network connection lightweight attention mechanism, has been proposed to effectively identify distinct types of weeds in maize seedling fields. The lightweight attention module is connected to the deep network of YOLOx-Darknet, which weakens the channel noise effect of residual computation and thus makes the detection model more efficient. A deconvolution layer has been introduced in the residual module to improve the small size feature extraction capability. The Generalized Intersection over Union (GIoU) has replaced the Intersection over Union (IoU) to minimize the positional discrepancy between the predicted frame and the actual frame, thereby enhancing the learning capacity of the detection model. Compared with the original algorithm, the improved one achieved an average detection accuracy of 94.86% in the performance evaluation, 0.07% better in the F1 value, and 1.16% better in the AP value. The weeding robot with an improved YOLOx algorithm had 92.45% detection rate of maize seedlings and 88.94% detection rate of weed recognition at 0.2 m s−1. The results of this study can provide technical references for real-time weed detection and robotic precision weeding.