Abstract

Object detection is a key computer vision task where the goal is to identify and localize objects within an image. While generic models like YOLO (You Only Look Once) can perform well on large, standard datasets (e.g., COCO), many real-world applications require detection of niche or domain-specific objects. This publication demonstrates how to adapt YOLO for a custom dataset—from annotation, training, and evaluation to deployment. Experiments on a custom dataset show competitive mean Average Precision (mAP) and inference speed, making our approach suitable for real-world scenarios such as industrial inspections, retail analytics, or specialized research projects.

Introduction

Object detection bridges the gap between image classification and localization, identifying where specific objects appear in an image. Traditional approaches rely on hand-engineered features and region proposals, which can be time-consuming and less robust. With the advent of deep learning, models like YOLO revolutionized object detection by offering real-time performance and end-to-end training.

Methodology

1. Data Collection and Annotation
   Data Source: Images were gathered from both public domain resources and real-world scenarios pertinent to the domain.
   Annotation Tools: Tools such as LabelImg or Roboflow were used to create bounding box annotations, which were then converted into YOLO format (text files containing class labels and bounding box coordinates).
2. Model Selection
   YOLO Architecture: YOLO (You Only Look Once) is chosen for its balance between speed and accuracy. YOLO treats object detection as a single regression problem, predicting class probabilities and bounding box coordinates simultaneously.
3. Training Configuration
   Framework: The implementation leverages PyTorch for easier reproducibility.
   Hyperparameters:
   Learning Rate: 0.001 (later tuned to 0.0005)
   Batch Size: 16
   Number of Epochs: 50 (adjusted based on early stopping or plateau of validation accuracy)
   Augmentations: Random scaling, flipping, and color jitter to improve robustness.
   Optimizer: Commonly, SGD or Adam is used depending on the preferred training stability vs. speed trade-off.
4. Evaluation Metrics
   Mean Average Precision (mAP): A standard metric for object detection that considers both precision and recall over multiple IoU thresholds.
   Precision & Recall: Provide class-wise performance insights.
   Inference Speed: Important for real-time applications, measured in frames per second (FPS).

Conclusion

This publication presents a comprehensive pipeline for training and deploying a YOLO-based object detection model on a custom dataset. By detailing every step—from data collection and annotation to model configuration, training, and evaluation—we aim to provide a template that can be adapted to various domains. Our experiments show that YOLO can effectively learn to detect objects unique to a specialized dataset, achieving competitive accuracy and real-time inference speed.