Comparison of YOLOv7 and YOLOv8 Architectures for Detecting Shirt Collars - Danyalson, Calvin; Cahyanti, Margi; Swedia, Ericks Rachmat; Sarjono, Mochammad Wisuda (Sebatik, 2024)
The shirt collar is one of the primary aspects monitored during online examinations in the postgraduate program at Gunadarma University. Examinees are required to wear formal, collared attire. Based on these regulations, a study was conducted to develop a collar detection method to facilitate the online exam monitoring process. This research involves a comparative analysis of two detection architectures: You Only Look Once (YOLO) version 7 (YOLOv7) and version 8 (YOLOv8), to determine the most effective architecture for detecting shirt collars using the dataset provided in the study. Detection models developed from both architectures were implemented in a web-based application and tested to evaluate their accuracy and efficiency. The testing results showed that YOLOv7 achieved an average accuracy of 95%, outperforming YOLOv8, which had an average accuracy of 75%. However, despite YOLOv8's lower accuracy, it excelled in detection speed, with an average processing time of 2.27 seconds, significantly faster than YOLOv7's average processing time of 22.42 seconds. Considering both accuracy and speed, YOLOv7 demonstrated the best overall performance in this study. Nonetheless, it is possible that YOLOv8 could surpass YOLOv7 in the future if significant improvements are made to its detection accuracy.