## 1 Introduction  
The issue of weeds inevitably impacts crop yields of agricultural production. In China, there are 1430 species (varieties) of agricultural weeds, leading to a reduction in grain production of up to 60 million tons and economic losses of up to USD 30 billion. Current main weeding control methods include biological weeding, chemical weeding, traditional mechanical weeding, and targeted weeding [1, 2, 3]. Biological weeding involves introducing foreign organisms to suppress weed growth, but these organisms can threaten local ecosystems [4]. Extensive application of herbicides plays a crucial role in controlling weeds and boosting agricultural efficiency, yet, this extensive application can induce resistance in weeds and significantly affect soil, biota, and human health [5]. Traditional mechanical weeding is effective pre-emergence, but post-emergence inaccuracy can irreversibly damage crops. Targeted weeding technology, which focuses on the specific locations of weeds, has gradually become a focal point in the era of precision agriculture and Agriculture 4.0 [6]. Current targeted weeding technologies can be categorized into targeted herbicide spraying and laser-targeted burning. With the rapid advancement of computer vision, deep learning convolutional neural networks have been widely implemented in targeted herbicide spraying [7; 8; 9; 10]. To enhance the accuracy of targeted weeding, researchers have optimized DeepLabv3, YOLOv7, the Deep Residual Convolutional Neural Network (DRCN), and the YOLOv4 backbone network, achieving detection accuracies of 91.53%, 94.96%, 97.3%, and 98.52%, respectively [11; 12; 13; 14]. Through precise targeted spraying, these enhancements have effectively reduced the amount of herbicides used. However, the use of herbicides cannot be completely eliminated, which contradicts the principles of sustainable green agriculture [15]. Laser-targeted weeding, as a branch of targeted weeding, has become feasible with the maturation of compact laser devices and the enhanced transferability of computer vision models. This technology allows for the precise control of lasers via high-speed visual computing on devices with limited computational power [10; 16; 17; 18]. Therefore, laser-targeted weeding methods are poised to become a more efficient and environmentally beneficial solution for weed management [19]. In earlier research, a color differentiation algorithm was used to extract plants from the soil background, followed by size differentiation to distinguish between crops and weeds [20]. This method proved to be inefficient and less effective in complex agricultural fields. [PERSON] et al. [21; 22] designed a neural-network-based blue laser weeding robot, incorporating a lightweight attention module into the YOLOv-Darknet architecture. Experimental tests showed a weed detection accuracy of 88.94% with a seedling damage rate of 4.53%. However, there is still significant room for improvement in the model's precision. [PERSON] et al. [23] compared the Yolov5 and SSD-ResNet50 networks using datasets of three crops and four weeds, with localization via bounding boxes. Tests demonstrated that the Yolov5-trained model achieved 88% detection accuracy and 27 FPS when transferred to the Nvidia Xavier AGX standalone device. In laser weeding operations, enhancing the model's accuracy is a crucial strategy to minimize seedling damage rates and improve production efficiency. The application of laser weeding has been predominantly restricted to bounding box localization (object detection) of weeds. Previous researchers have endeavored to enhance object detection algorithms such as ResNet and YOLO series. Their goal was to calculate the central position of weeds based on the rectangular coordinates obtained from object detection, thereby guiding the laser to accurately target the weeds, meeting practical application requirements. However, object detection methods have consistently fallen short in precisely delineating the edges of weed stems and leaves. [PERSON] et al.