# Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study

arXiv: https://arxiv.org/abs/2505.11424 · HTML: https://mlsanity.com/paper/2505.11424/

## 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 (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving](https://mlsanity.com/paper/2011.10671/) (2020)
- [A Review of Object Detection Models based on Convolutional Neural Network](https://mlsanity.com/paper/1905.01614/) (2019)
- [Optimizing the Trade-off between Single-Stage and Two-Stage Object Detectors using Image Difficulty Prediction](https://mlsanity.com/paper/1803.08707/) (2018)
- [YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain](https://mlsanity.com/paper/2406.10139/) (2024)
- [Optimizing RPL Routing Using Tabu Search to Improve Link Stability and Energy Consumption in IoT Networks](https://mlsanity.com/paper/2408.06702/) (2024)

## Abstract (arXiv)

This study evaluated the performance of a YOLOv8-based segmentation model for detecting and segmenting wrinkles in facial images.