ISEE Vision Blog

Freight elevator safety: what OSHA, ASME A17.1 and EN 81-31 say about riders, plus five controls compared, from key switches and door locks to AI cameras.

CCTV vs AI camera in a factory: one records what happened, the other acts while it happens. See how they differ, where each belongs, and how to combine them.

AI safety camera alerts only help if they reach the person at risk. Compare dashboard notifications with relay and PLC outputs, second by second.

Suspended load safety starts with keeping people out from under the hook. Compare OSHA and EU rules, warnings, barriers, signalers and crane-mounted cameras.

Employer PPE obligations under EU Directive 89/656/EEC and OSHA 1910.132: hazard assessment, free provision, training, supervising use and keeping records.

How a photo eye sensor protects people at an industrial door, what EN 12453 and UL 325 expect, where photo eyes fall short, and what an AI camera adds.

The 7 HACCP principles explained, with Codex, FDA and EU 852/2004 references, and why kitchen hygiene rules like hairnets and handwashing are hardest to audit.

NPN vs PNP sensor outputs, light-on and dark-on, dry contacts, and why a standard relay can't do a safety relay's job. A plain wiring guide for plant engineers.

OHS in Turkey explained for foreign employers: Law 6331 duties, hazard classes, OHS specialists, training, accident reporting and work stoppage. Learn more.

Workplace safety signs explained: ISO 7010 shapes and colors, EU Directive 92/58/EEC, OSHA 1910.145, where to mount signs, and why signs alone are not enough.

Industry 4.0 examples from real plants: sensors, AI decisions, robots, and vision inspection, with measured results and a simple way to pick your first project.

What is quality control? Learn how QC differs from QA, the three inspection stages, where manual inspection fails, and how automated quality control works.

What people mean by a "defect" in a tablet or capsule, one by one, and how each shows up in an image.

In medical packaging, "we removed the defective product" isn't enough. You need to show what you removed, why, and when. This post is about how image records change the quality team's work.

The same questions come up every time we discuss AI inspection for caps and bottles. Here are our answers, without overselling.

No two food products look exactly alike. To find the defect, stop asking "what does a defect look like?" and start asking "what does a good product look like?"

Anyone who has spent eight hours at an inspection table knows the problem is not the method. It is the limits of the person. Knowing those limits shows you where and how to support the inspection.

In furniture, the craftsman knows quality best. But the craftsman's eye cannot be at every piece. The challenge is carrying that knowledge into series production.

One crack is a scrap part. The same crack in the same place, again and again, is a message.

Every system looks good in a demo. The real difference shows up on your surface, under your lighting, and at your next product changeover.

A scratch on flat sheet is easy to see. The trouble starts when the surface itself is made of thousands of "lines" and "holes."

In automotive, the cost of a defective part is rarely the part itself. It is the questions that follow. This post walks through 8D problem solving step by step and shows where part-level visual records strengthen the supplier's hand.

When we talk with pipe and hose makers about in-line visual inspection, we hear the same objections. Each one has some truth in it. None of them alone is enough to say "checking by eye is fine."

A cable defect is small, but once it is wound onto a reel it becomes the whole reel's problem. This post works backward from a return complaint to where the defect should have been caught.

Choosing an industrial AI camera system? The 9 questions to ask every vendor, what a good answer sounds like, a pilot checklist and a bid comparison table.

A practical guide to machine guarding: the four guard types, safeguarding devices such as light curtains and two-hand controls, what OSHA 1910.212 requires, and where AI vision fits as an additional layer.

Forklift accident statistics from BLS, NIOSH, OSHA and HSE: annual deaths, the most common fatal accident types, OSHA citations and UK workplace transport data, each with its source and data year.

What counts as a near miss, why reporting programs miss most of them, what a good report contains, and how AI cameras log near misses automatically.

How an industrial blind spot monitoring system works: where forklift blind spots form, OSHA and EU rules, and how mirrors, sensors, tags and AI cameras compare.

How does vehicle speed detection in industrial areas actually work? Compare speed limiters, geofence zones, and AI cameras. Schedule a free assessment today.

Vehicle identification in industrial areas isn't one technology, it's three layers. Compare ANPR, RFID, and AI vision. Schedule a free site assessment.

How does traffic management in industrial areas work? Build a layered-defense plan with segregation, speed control, and AI cameras. Get a free assessment.

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.