Naslov
Evaluating YOLOV5, YOLOV6, YOLOV7, and YOLOV8 in Underwater Environment: Is There Real Improvement?

Autori
Gašparović, Boris ; Mauša, Goran ; Rukavina, Josip ; Lerga, Jonatan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Skup
SpliTech2023 - 8th International International Conference on Smart and Sustainable Technologies

Mjesto i datum
Bol, Hrvatska, 20.06.2023. - 23.06.2023

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Artificial Intelligence ; Machine Learning ; Object Detection ; Underwater Images

Sažetak
This paper compares several new implementations of the YOLO (You Only Look Once) object detection algorithms in harsh underwater environments. Using a dataset collected by a remotely operated vehicle (ROV), we evaluated the performance of YOLOv5, YOLOv6, YOLOv7, and YOLOv8 in detecting objects in challenging underwater conditions. We aimed to determine whether newer YOLO versions are superior to older ones and how much, in terms of object detection performance, for our underwater pipeline dataset. According to our findings, YOLOv5 achieved the highest mean Average Precision (mAP) score, followed by YOLOv7 and YOLOv6. When examining the precision-recall curves, YOLOv5 and YOLOv7 displayed the highest precision and recall values, respectively. Our comparison of the obtained results to those of our previous work using YOLOv4 demonstrates that each version of YOLO detectors provides significant improvement.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA