An Introduction to Liquid Spills 

Liquid spills, particularly those involving petroleum products, chemicals, and hazardous materials, are common in industrial settings. For example, in manufacturing plants, oils and lubricants are likely to spill near machinery, creating slippery surfaces. In chemical plants, liquid spills can result in contamination or exposure to harmful chemical substances. Water leaks are common in food processing plants. 

Spills can lead to significant safety hazards and operational disruptions, as well as impact the environment. Each type of spill presents unique risks, such as fire hazards from flammable liquids or toxic exposure from chemical spills. The economic consequences can also be substantial. Cleanup costs, regulatory fines, and potential litigation can strain a facility's finances. 

While specific statistics on injuries directly caused by liquid spills in factories are limited, broader data on workplace injuries indicates that slips, trips, and falls—often resulting from liquid spills—are among the leading causes of workplace accidents. The Bureau of Labor Statistics (US) reports that slips and falls accounted for approximately 26% of all workplace injuries in recent years. Across the globe, according to the National Safety Council, the total cost of work-related injuries was an estimated $167 billion in 2022, which included various components such as wage and productivity losses, medical expenses, and administrative costs. 

How does a facility develop a response plan to this problem?

Manual inspections and traditional surveillance systems, once the standard for spill detection, are now outdated and inefficient. These methods are not equipped to reliably detect hazards like liquid spills and are hindered by human error, camera blind spots—areas not covered by current cameras or patrols—environmental factors such as dust, mist, or high temperatures, and slow response times. Additionally, older computer vision models often lack the accuracy needed for effective detection, making reliable identification of hazards difficult, if not impossible.

In contrast, real-time detection and reporting powered by advanced computer vision technologies are spot-on when it comes to mitigating workplace hazards. Today, vision models have advanced significantly with the development of Vision Transformers (ViTs). These models leverage self-attention mechanisms to capture intricate spatial relationships in images, making them highly effective for complex tasks like liquid spillage detection. Additionally, the integration of ViTs with real-time multi-camera video streams allows for dynamic and accurate hazard detection even in challenging industrial environments with varying lighting and cluttered backgrounds.

In this article, we will explain how the latest advancements in Vision AI are transforming workplace safety. We will also highlight how Awareye is empowering enterprises to achieve this transformation with our highly scalable Vision AI platform.

Advancements in Vision AI Systems

Let’s begin by first looking at how cutting-edge vision AI systems, particularly those leveraging Vision Transformers (ViTs), operate. Unlike traditional Convolutional Neural Networks (CNNs) that rely on localized filters to extract spatial features, Vision Transformers use self-attention mechanisms to capture global relationships within an image. 

ViTs treat image patches as a sequence of tokens, similar to words in large language models (LLMs), and apply attention across these patches. This allows them to effectively model both short-range and long-range dependencies, providing more context-aware and detailed feature extraction, making them ideal for complex environments such as factory floors where lighting conditions, reflections, and varying textures can obscure liquid spills. 

Other than ViTs, there are models like YOLO (You Only Look Once) or ResNet-50, which have also demonstrated high precision in tasks like object detection. The Hugging Face model leaderboard is a great resource to explore the trending vision models, all of which can be used with DeepStream and Awareye.

DeepStream SDK: Real-Time Processing for Multi-Camera Vision AI

To deploy such advanced vision models at scale, particularly in industrial environments where hundreds or even thousands of cameras may be involved, NVIDIA’s DeepStream SDK offers a powerful approach. 

DeepStream is designed for high-throughput, real-time AI-powered video analytics, enabling the deployment of vision models across multiple camera streams. With built-in support for hardware acceleration using NVIDIA’s latest GPUs, DeepStream efficiently processes video streams, handling tasks such as object detection, classification, and anomaly detection—including spill detection—either at the edge or in the cloud.

Multi-Camera Vision AI

DeepStream is built on the popular GStreamer framework, which allows it to ingest and process video from various sources, including RTSP streams from commodity cameras. This is crucial for scalable multi-camera Vision AI systems, as many existing factory setups already use such cameras. As long as these cameras can stream video using RTSP (Real-Time Streaming Protocol) or similar protocols, they can be seamlessly integrated into the Vision AI pipeline without the need for costly hardware upgrades.

You can even create multi-camera systems configured to monitor different sections of a factory or worksite. Each camera stream can be processed in parallel, with DeepStream managing the synchronization and inference across multiple streams. The platform’s use of batching techniques ensures efficient handling of large data volumes, while TensorRT optimizes the deployment of deep learning models, including Vision Transformers.

Commodity Cameras and Their Integration

One of the most significant advantages of modern Vision AI systems is their ability to work with commodity cameras, as long as these devices can provide a standard video stream (e.g., via RTSP). This is transformative for industries that cannot afford to replace existing camera infrastructure. Platforms like Awareye, built using several containerized and configured DeepStream nodes, can ingest video from virtually any camera capable of real-time streaming, including commodity cameras. This means that businesses can avoid the cost of overhauling their surveillance systems while still benefiting from cutting-edge AI capabilities.

Commodity cameras, when paired with modern Vision AI architecture, can handle complex tasks such as liquid spill detection, people tracking, and safety compliance in real-time. The ability to deploy AI models, such as ViTs, across large camera networks with minimal latency ensures that hazardous situations like spills are detected and addressed immediately.

How Awareye’s Vision AI Helps With Liquid Spillage Detection

The Awareye on-premise architecture is designed for efficient multi-camera Vision AI deployment across industrial sites. The technology works by orchestrating pre-configured DeepStream and customized ViT vision models into containers, which are capable of ingesting numerous camera feeds. It can then monitor and generate real-time actions, which can be integrated into various alerting setups or third-party systems.

Since Awareye can ingest hundreds of camera feeds, the system can be deployed on-premise with camera setups that cover your entire factory floor. The models have been specifically trained to detect liquid spills, enabling the system to achieve much higher precision than traditional vision models.

Here are some aspects of Awareye that are particularly useful in spillage detection scenarios.

Real-Time Monitoring with Awareye Vision AI

Awareye Vision AI is not a traditional surveillance system that relies on human supervision. Instead, it provides continuous, real-time monitoring of the entire facility. Using advanced vision algorithms, the system can instantly detect a spill and alert the relevant personnel, eliminating the delay between the occurrence of the spill and its detection.

If you are already using an existing camera system, we can help you integrate Awareye fairly easily. Our platform seamlessly augments your existing setup, causing minimal disruption.

Working with Existing Camera Systems for Efficient, Cost-Effective Deployment

One of the key advantages of Awareye Vision AI is its ability to integrate with pre-existing camera systems. You don’t need to invest in expensive cameras costing $1,000 to $10,000, unless your current systems are really outdated.

Awareye works with your existing security cameras, enhancing them with AI capabilities to improve detection. This means your implementation overhead is low, while still allowing you to significantly enhance your factory’s safety protocols.

Continuous, Automated Detection of Spillage

Since Vision AI systems can operate 24/7, 365 days a year, you can ensure continuous monitoring of factory floors. This means spills can be detected at any time, whether the factory is in operation or temporarily closed. 

For sites with high levels of foot traffic or machinery movement, this provides peace of mind, knowing that safety is always being monitored.

Detecting Small Spills and Pooling Liquids in Challenging Environments

Not all spills are large or immediately visible to the human eye. A small leak or pooling liquid, especially in low-light environments, might go unnoticed during manual inspections until it becomes a larger problem.

Awareye Vision AI can detect even small leaks before they escalate into bigger hazards. Its ability to function effectively in poorly lit or crowded areas ensures comprehensive coverage. Our fine-tuned models can accurately differentiate between reflections, shadows, debris on the floor, and actual liquid spills.

Multi-Camera System for Full Site Coverage and Blind Spot Reduction

Blind spots are a critical vulnerability in traditional surveillance systems, as they are areas not covered by existing cameras or manual inspections. These gaps can occur due to various factors, such as poor camera placement, obstructions in the environment, or limitations in the field of view of individual cameras. In industrial settings, blind spots can pose serious safety risks, particularly in high-traffic or hazardous areas where early detection of issues like spills, equipment malfunctions, or safety violations is crucial.

Awareye eliminates the problem of blind spots by utilizing multiple cameras positioned strategically across the facility. This ensures that every corner of the workspace is monitored, whether it's a small workshop or a large industrial complex. The system is highly scalable, meaning it can easily adapt to the size and layout of any facility. 

Conclusion

At Awareye we bring the power of Vision AI to enterprises in a simple, highly scalable package, offering a distinct competitive edge. Our solution enables factories to detect and resolve spill hazards with unmatched precision and speed. By integrating Awareye into your operations, you not only improve workplace safety but also enhance overall operational efficiency by staying proactive against unforeseen risks and minimizing downtime.

With Awareye, you can transform your safety protocols from reactive to proactive, addressing hazards before they escalate into serious issues. This leads to fewer accidents, reduced liability, and a safer work environment. To take the next step in improving your workplace safety with Awareye Vision AI, reach out to us for a demo.