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This article provides a detailed guide to deep learning-based object detection using the YOLOv5 algorithm. It covers every step from preparing the data.yaml file and selecting the model to configuring training parameters and interpreting training output graphs. The real-time inference process is also explained with example commands.

This article provides a detailed explanation of the dataset creation and labeling process required for object detection using the YOLO algorithm. It covers the importance of collecting quality data, the use of labeling tools, the structure of the YOLO format, dataset splitting (train/valid/test), and methods to prevent overfitting with examples.

This article thoroughly explores the concept of computer vision, the object detection process, and commonly used algorithms. It provides a step-by-step explanation of how the YOLO algorithm works, how bounding boxes are generated, and how confidence scores are calculated, along with comparisons to other algorithms.

This blog analyzes the causes of workplace accidents in logistics loading zones and explains how computer vision systems can be used to prevent them.

This blog outlines essential factory safety protocols, emphasizing the importance of AI-assisted systems in reducing accidents and enhancing productivity.

Welding operations are on the rise, and so are occupational accidents. From small shops to industrial giants, welding safety is critical.

The steel industry is one of the heaviest production industries. Massive metal blocks are moved and melted by giant machines.

The MQTT protocol was developed in 1999 for the oil and gas industry. Engineers needed a protocol that consumed minimal bandwidth and energy for monitoring pipelines via satellite.