# An Object Detection and Localization Method Based on Improved YOLOv5 for the Teleoperated Robot
## 1 Introduction  
The teleoperated robot can help people to complete some complex operation tasks in dangerous or difficult-to-reach environments. By combining human operation experience and judgment ability with robot intelligence, it can overcome the limitations of human physiology and psychology, and environmental geographic space. It greatly expands the human perception ability and operation means. The teleoperated robot is widely applied in demining and detonation, radiation pollution, public health, and other fields [1, 2, 3, 4, 5, 6], which can improve work efficiency and prevent operators from being hurt. In the teleoperation system, the robot is often equipped with a robotic arm to allow the robot to adapt to various complex operations. Given the working space limitation of the robotic arm, precise object localization is required for the teleoperated robot. The object localization accuracy is an important factor that affects the execution efficiency and success rate of teleoperation tasks. In the traditional teleoperation system, during the task execution, the operator mainly relies on the real-time scene information returned from the robot terminal to flexibly adjust the position of the robot, so that the object is in a suitable working space, after which the operator controls the robot to complete the task. Since the object localization largely depends on the operator's experience and on-the-spot judgment, the localization accuracy is low and the execution efficiency is slow. Computer vision technology can give mechanical systems more powerful and intelligent object recognition capabilities, so it is widely used in robotics, intelligent detection, aerospace, agricultural picking, and other fields [7; 8; 9; 10; 11; 12]. At present, the two-stage and one-stage object detection algorithms comprise the majority of the object detection algorithm. Although the latter is greater in terms of detection speed, the former is superior in terms of detection and localization accuracy. The R-CNN [13], SPP-Net [14], Fast R-CNN [15], Faster R-CNN [16], Mask R-CNN [17], and A-Fast-RCNN [18] are the major elements of the two-stage object detection algorithm. The OverFeat [19], YOLO [20], YOLOv2 [21], SSD [22], R-SSD [23], YOLOv3 [24], YOLOv4 [25], and YOLOv5 [26] are the major elements of the one-stage object detection algorithm. The object detection method is widely applied in robotics because it can rapidly and precisely locate the object within the vision system's field of view, enhancing the system's intelligence and task execution efficiency. In [27], an improved YOLOv3-based litter detection method is proposed for litter-capturing robots, achieving real-time high-precision object detection in dynamic aquatic environments. In [28], an intelligent detection algorithm for seafood is proposed for the underwater robot, which achieves the intelligent detection of underwater objects and guides underwater robots to autonomously grasp seafood. In [29], a high-precision strawberry detection algorithm based on an improved Mask R-CNN is proposed for the fruit picking robot, which solves the problems of poor generality and robustness of traditional object detection algorithms in strawberry detection. However, this research and these applications are mainly oriented to the autonomous grasping robot, and pay more attention to the robustness and accuracy of object detection. In the teleoperated robot, the object localization accuracy and detection speed are important factors that affect task execution efficiency and the operator's interactive experience. Therefore, we propose an object detection and localization method based on the improved YOLOv5 network model for the teleoperated robot. The following is a summary of our work's main contributions:  
1. Improve the classic YOLOv5 object detection network model; the improved model can effectively increase the accuracy and detection speed of object detection. 2.