ISEE Vision Blog

Build the AI safety business case: quantify incident savings, insurance reductions, and productivity gains with our manufacturing ROI framework.

Forklift collision prevention, dock safety, and PPE compliance for warehouses and logistics facilities. See how AI monitoring protects workers 24/7.

How AI vision systems protect workers in steel mills — from crane proximity alerts to PPE compliance monitoring. Reduce incidents, ensure ISO 45001 complianc...

Compare edge AI vs cloud safety systems on latency, privacy, cost, and reliability. Find the right architecture for your industrial facility.

How AI safety solutions for automotive manufacturing protect workers from robots, press machines, and paint booth hazards — with real deployment data.

Comprehensive guide to AI-powered industrial safety systems: how computer vision prevents accidents, ensures compliance, and protects workers.

PDPL compliance guide for industrial AI camera systems, data privacy requirements, and the privacy advantages of Edge AI.

A detailed comparison of light curtains and AI camera safety systems for press machines. Hand detection, muting challenges, and bypass risks analyzed.

UWB tag-based systems vs AI camera solutions for forklift-pedestrian collision avoidance. Cost, installation, and effectiveness compared.

Technical comparison of AI cameras vs light curtains, laser scanners, and UWB systems. A guide to choosing the right safety solution.

How AI technologies are transforming industrial workplace safety, the advantages of AI camera systems, and ROI analysis.

A review of why occupational accidents remain high in Turkey based on 2025 data and how AI-powered camera systems deliver preventive safety in industrial environments.

A practical overview of the risks around overhead and bridge cranes, the limits of traditional safeguards, and how AI-powered camera systems can prevent incidents before they happen.

A clear and data-driven analysis of why accidents still occur around dangerous industrial machines, despite conventional safety measures. This article explains the hidden limitations of sensor-based protection systems and demonstrates how AI-powered camera technologies deliver a fundamentally safer, more reliable, and production-friendly approach. By focusing on continuous area monitoring, manipulation detection, and limb-level risk analysis, it shows how modern facilities can prevent accidents before they happen—without sacrificing efficiency.

This blog post discusses the current regulations in force regarding data security and management in artificial intelligence solutions. By explaining GDPR, KVKK, and ISO/IEC 42001 standards in their basic aspects, it clearly and concisely highlights why personal data protection, ethical AI management, and legal compliance processes are critical for organizations.

This blog post explains how artificial intelligence plays an effective role in Personal Protective Equipment (PPE) detection and its contributions to occupational health and safety processes. It describes how ISEE-CAM’s AI-based object detection technology enables automatic monitoring of PPE usage, instant alerts in cases of missing or incorrect equipment, and the creation of safer and more sustainable working environments to prevent workplace accidents.

This blog post discusses the main causes of forklift accidents in industrial environments and examines the impact of driver negligence and rule violations on occupational health and safety. It explains how ISEE-CAM’s advanced computer-vision-based forklift safety modes (Modes 4–17) prevent accidents through human, vehicle, and zone detection solutions and improve safety in working environments.

This article comprehensively explains the importance, application areas, historical development, and technical approaches of computer-vision-based AI anomaly detection in industrial production lines, highlighting its advantages in time, cost, and reliability for quality control processes.

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.