Toward Enhanced Support for Ship Sailing

Cafaro, Massimo; Epicoco, Italo; Pulimeno, Marco; Sansebastiano, Emanuele

doi:10.1109/access.2023.3303808

Cited by 4 publications

(1 citation statement)

References 81 publications

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“…For ABOships, Cafaro et al 62 employed the real-time detection model YOLOv6n and achieved relatively low performance with mAP0.5 and mAP0.5:0.95 scores of 0.539 and 0.251, respectively. Its performance was even poorer in the case of two-stage detectors, with the best performance of AP 0.352 achieved by Faster RCNN + Inception ResNet V2 11 .…”

Section: Resultsmentioning

confidence: 99%

A streamlined approach for intelligent ship object detection using EL-YOLO algorithm

Yang,

Solihin,

Ardiyanto

et al. 2024

Sci Rep

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Maritime objects frequently exhibit low-quality and insufficient feature information, particularly in complex maritime environments characterized by challenges such as small objects, waves, and reflections. This situation poses significant challenges to the development of reliable object detection including the strategies of loss function and the feature understanding capabilities in common YOLOv8 (You Only Look Once) detectors. Furthermore, the widespread adoption and unmanned operation of intelligent ships have generated increasing demands on the computational efficiency and cost of object detection hardware, necessitating the development of more lightweight network architectures. This study proposes the EL-YOLO (Efficient Lightweight You Only Look Once) algorithm based on YOLOv8, designed specifically for intelligent ship object detection. EL-YOLO incorporates novel features, including adequate wise IoU (AWIoU) for improved bounding box regression, shortcut multi-fuse neck (SMFN) for a comprehensive analysis of features, and greedy-driven filter pruning (GDFP) to achieve a streamlined and lightweight network design. The findings of this study demonstrate notable advancements in both detection accuracy and lightweight characteristics across diverse maritime scenarios. EL-YOLO exhibits superior performance in intelligent ship object detection using RGB cameras, showcasing a significant improvement compared to standard YOLOv8 models.

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Section: Resultsmentioning

confidence: 99%