# Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics
## 2 Development of the Fumigation Robot
### Training Hotspots Using YOLOv8  
Any autonomous robot that navigates must perceive the environment and obtain details to act accordingly. To serve this purpose, there are different detection algorithms, such as Faster Region-based Convolutional Neural Network (Faster R-CNN) [40], Faster R-CNN VGG-16 [41], faster deformable part model (Fastest DPM) [42], and YOLO [43]. In [43], experimental results on the visual object classes dataset showed that Fast-YOLO, one of the successors of the well-performing model YOLO, was able to perform better with an _mAP_ score of 63.4% and 155 frames per second, which was, on average, two times faster than other detection models. There have been many versions of YOLO, and for the proposed system, the YOLOv8 algorithm was used, and it proved to be faster and lighter than its predecessors. The detected objects using YOLOv8 are put inside the map to identify the fumigation hotspots. The YOLOv8 is a cutting-edge object detection model known for its exceptional speed and accuracy, making it ideal for real-time applications. Building on the strengths of its predecessors, YOLOv8 introduces advanced features, such as anchor-free detection, which simplifies the model and enhances its generalization capabilities. It leverages the latest advancements in deep learning architectures, including Cross-Stage Partial (CSP) connections and Path Aggregation Network (PAN), for superior feature extraction and aggregation.  
The architecture of YOLOv8, as shown in Figure 3, is designed for efficient and precise object detection, comprising a backbone, neck, and head. The backbone incorporates EfficientRep blocks inspired by MobileNet and EfficientNet, utilizing depthwise separable convolutions and squeeze-and-excitation modules to optimize performance. It also includes Reparameterized VGG block (RepVGG) blocks, simplifying complex structures for improved inference efficiency. The neck features convolutional blocks enhanced with activation functions like leaky rectified linear unit, which introduce non-linearity and assist in primary feature extraction. RepBlocks, configured differently for training and inference, facilitate better learning while reducing computational load. The upsampling layers increase the spatial dimensions of the feature maps, which is crucial for detecting smaller objects.  
The head of YOLOv8 features an Efficient Decoupled Head, which separates classification and localization tasks into distinct branches, leading to more accurate detections. This architecture ensures that the model identifies and classifies objects, making it a powerful tool for real-time detection applications. A database keeps track of the fumigation locations. The robot uses this information to generate a prioritized sequence order to navigate from one point to another and fumigate in the shortest time. All the operations on the robot side that perform navigation and fumigation are controlled using ROS. As the robot is deployed in semi-outdoor and outdoor scenarios, SLAM algorithms like High-Definition LiDAR (HDL) graph slam map the environment and later localized using HDL localization for precise accuracy.