# Enhanced YOLO- and Wearable-Based Inspection System for Automotive Wire Harness Assembly
## 1 Introduction
### Related Work  
In recent years, with the rapid advancement of artificial intelligence [5] and computer vision [6], the integration of machine vision and deep learning algorithms has spearheaded an era of innovative upgrades. In the field of industrial production, an increasing number of visual detection systems employing deep learning are being applied [7; 8; 9]. The study presented in [10] explores the application of deep learning in the Automated Optical Inspection (AOI) of ceramic substrates in circuit images, aiming to develop an automated defect detection system capable of identifying types and locations of defects. Ref. [11] developed a novel automated computer vision algorithm and system for inspecting component replacements in printed circuit boards. To enhance the efficiency and accuracy of the assembly process, more automotive and wiring harness manufacturers are beginning to incorporate machine vision hardware and software, replacing traditional manual operations. Research studies [12; 13; 14] investigated new methods and systems of computer vision applied in the assembly process of automotive wiring harnesses. Typical industrial computer vision systems utilize stationary or motion modules and robotic arm-mounted industrial cameras to capture image information of the target objects. With the continuous development of smart manufacturing, wearable image acquisition devices are becoming increasingly popular in industrial applications due to their higher flexibility and stronger adaptability. The vision inspection system for automotive wiring harness assembly needs to perform detection in tandem with the movements of the assembly personnel, requiring the system's detection algorithm to adapt well to dynamic scenes. In the realm of deep-learning-based detection of targets in motion, the industrial sector primarily employs two types of object detection algorithms: the two-stage object detection algorithms, exemplified by Faster Region-Convolutional Neural Network (R-CNN) as proposed by [PERSON] et al. [15]; and the single-stage object detection algorithms, represented by YOLO, as introduced by [PERSON] et al. [16]. The single-stage object detection algorithm, with its streamlined network architecture, achieves a remarkably high detection speed. It reaches a frame rate of 45 on VOC2007, significantly surpassing Faster R-CNN, which only achieves a frame rate of 7. This efficiency has led to its extensive application across various scenarios [17; 18; 19]. Regarding enhanced single-stage target detection algorithms tailored for specific tasks, refs. [20; 21; 22] introduce the attention module and feature fusion module, enhance the multi-scale feature network, modify the Backbone network, and employ other measures to improve the YOLO model and enhance the performance of the detection algorithm. The primary contribution of this paper lies in the design of a wearable vision inspection system for automotive wiring harness assembly. This system assists workers in efficiently completing the inspection tasks of wiring harness assembly without adding extra workload. It is specifically intended for the detection of various assembly components, including the automotive wiring harness number, relay box labeling, relay types, the number of relays, and the color and size of the relays, focusing on aspects such as characters, colors, and shapes. This paper investigates the performance of various mainstream networks and finds that the YOLOv5s [23] network has significant advantages in handling complex motion scenarios and dynamic multi-object detection. Therefore, YOLOv5s was chosen as the base network. To address issues specific to automotive wiring harness assembly scenarios, such as image motion blur and significant variations in target image sizes, key modules of the YOLOv5s network were optimized. Firstly, the Detect module of YOLOv5s was integrated with the Adaptive Spatial Feature Fusion Network (ASFF) [24], effectively utilizing features at different scales.