YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection
От YOLO-v1 до YOLO-v8: развитие YOLO и его взаимодополняющая роль в цифровом производстве и обнаружении промышленных дефектов
2023-06-23
SCID: 54.1/2csqwyky
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YOLO object detectorsautomated quality inspectiondigital manufacturingedge devicesindustrial surface defect detection
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Abstract (AI)
Since its inception in 2015, the YOLO (You Only Look Once) variant of object detectors has rapidly grown, with the latest release of YOLO-v8 in January 2023. YOLO variants are underpinned by the principle of real-time and high-classification performance, based on limited but efficient computational parameters. This principle has been found within the DNA of all YOLO variants with increasing intensity, as the variants evolve addressing the requirements of automated quality inspection within the industrial surface defect detection domain, such as the need for fast detection, high accuracy, and deployment onto constrained edge devices. This paper is the first to provide an in-depth review of the YOLO evolution from the original YOLO to the recent release (YOLO-v8) from the perspective of industrial manufacturing. The review explores the key architectural advancements proposed at each iteration, followed by examples of industrial deployment for surface defect detection endorsing its compatibility with industrial requirements.
Key Findings
1
Successive YOLO variants increasingly address industrial inspection requirements, including fast detection, high accuracy, and deployment on resource-constrained edge devices.
2
The paper provides the first in-depth review of YOLO’s evolution through YOLO-v8 specifically from an industrial manufacturing perspective.
3
The review analyzes architectural advances across YOLO iterations and presents industrial surface-defect detection deployments demonstrating compatibility with manufacturing requirements.
4
YOLO detectors have evolved from YOLO-v1 to YOLO-v8 while preserving their core focus on real-time detection, high classification performance, and computational efficiency.
Research Object
YOLO object-detection variants (YOLO-v1 through YOLO-v8) applied to automated industrial surface-defect inspection
Research Subject
The evolution of YOLO architectures and their suitability for industrial surface-defect detection, including real-time performance, classification accuracy, computational efficiency, and deployment on constrained edge devices
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2023-06-23
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