RCA: YOLOv8-Based Surface Defects Detection on the Inner Wall of Cylindrical High-Precision Parts

Li, Wei; Solihin, Mahmud Iwan; Nugroho, Hanung Adi

doi:10.1007/s13369-023-08483-4

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…In the future, we will continue to investigate ship detection under diverse weather conditions to further improve the model's performance and generalization capability. The streamlined approach of YOLO-based object detection can also be explored for other applications such as in ground autonomous vehicles 63 and in object detection in tiny objects 64 .…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%