Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study
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Abstract (arXiv)
This study evaluated the performance of a YOLOv8-based segmentation model for detecting and segmenting wrinkles in facial images.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 48 references that are in the map. First posted on arXiv 16 May 2025.
Builds on
7 of 48 references.
References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015SSD: Single Shot MultiBox Detectorcited by 3,130
- 2020A Review and Comparative Study on Probabilistic Object Detection in Autonomous Drivingcited by 115
- 2019A Review of Object Detection Models based on Convolutional Neural Networkcited by 22
- 2018Optimizing the Trade-off between Single-Stage and Two-Stage Object Detectors using Image Difficulty Predictioncited by 18
- 2024YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domaincited by 14
- 2024Optimizing RPL Routing Using Tabu Search to Improve Link Stability and Energy Consumption in IoT Networkscited by 2
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