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From worker safety to detecting product defects to overall quality control, industrial and visual inspection plays a crucial role. Pharmaceutical and cosmetic manufacturing, food production, heavy machinery operation, energy production, electronics manufacturing, and more, differ significantly in the products and services they offer, yet they all recognize inspection is vital for promptly detecting issues and ensuring that processes operate efficiently according to design.
Efficient processing and management of data across various modalities, including text, images, video, and audio, are critical for effective applications of visual and industrial inspection. This multimodal data in combination with the rapidly improving AI techniques can be particularly powerful, as it allows for a more comprehensive analysis by combining information from different sources. Improvement opportunities and benefits are vast and vary greatly based on the type of inspection being done.
Multimodal AI Use Cases For Industrial And Visual Inspection
Worker Safety
Workers may not always comply with required Personal Protective Equipment (PPE). There might be hazardous spills or obstructions creating an unsafe working environment. Applying visual inspection to worker safety can protect employees from work related illnesses and injuries, boost morale and efficiency, and improve regulatory compliance.
If AI models can detect PPE violations and environmental hazards from a camera feed as they happen and generate alerts, safety issues can be immediately identified and rectified in real time. This can be made possible at scale with AI models trained to detect people, products, and their interactions with all the camera and sensor data available.
Defect Detection and Quality Control
No one wants a defective product - not the consumer, not the retailer, and most importantly, not the manufacturer. Visual detection can be used to identify manufacturing defects more effectively and sooner, reducing waste, safeguarding quality, and improving costs.
Cameras and other sensors along manufacturing lines capture a variety of data in addition to images or videos, monitoring products and machinery at different stages of production. AI models trained on this multimodal data can capture defects more effectively than individual sensors acting independently.
Predictive Maintenance
Many businesses rely on large, expensive systems that can be difficult and/or expensive to monitor and maintain, such as an oil rig in a remote area. If these systems break down, it may result in a catastrophic spill or fire, endangering not just the workers but also the surrounding communities with devastating environmental impacts.
A tremendous amount of data comes from these machines including performance data, product data, throughput data, cameras focused on difficult to access areas of the machine, and audio recordings of the machine in operation. All of this multimodal data can be used to build and train AI models to identify operational abnormalities and potential equipment defects. This results in proactively identifying and addressing anomalies quickly, before they become emergencies, cause millions in damages, or worse, result in loss of life, due to large equipment failures.
Industrial And Visual Inspection Challenges Facing Data Scientists And AI Teams
Regardless of the specific use case, multimodal AI has become increasingly important for industrial and visual inspection as AI allows you to achieve your goals faster, yet it is not without cost and depends on quality data. While the specific goals vary, all focus on improving efficiency and performance in operations, lowering overall costs, optimizing resources, and ultimately driving business growth and revenue. As AI algorithms and models are seeing rapid improvements, some common challenges remain, to prove value and deploy in production:
Disparate Data Sources: Collecting industrial data for detection or training can often require ingestion of data from many different endpoints, sending data at different frequencies and in different formats. These data sources are continuously getting richer as cameras and sensors improve. Data management solutions and data loading pipelines need to support this evolving information from disparate sources with ease.
Dataset Versioning: Models need iterations as data evolves. Often, it is necessary to create datasets using complex searches that involve vector similarity to find similar defects in images and so on. Equally important is to manage and define datasets according to the state of the data, and track versions of these datasets.
Knowledge Loss: Departure of experienced team members can create knowledge gaps, and processes can become non-repeatable or ad-hoc due to inadequate tooling. Onboarding new resources to work with complex tooling becomes extremely frustrating and time-consuming, impacting the success of ongoing AI projects.
Rising Costs: Cloud costs are on the rise, affecting the cost vs. benefit calculus of multimodal data. Effective resource utilization and tooling are vital to safeguard return on investment (ROI) as expenses rise.
Scaling and Growth: Scaling to large volumes poses challenges, and achieving high performance can be exceptionally difficult in the realm of multimodal data.
Despite advancements in data science and machine learning, the success of AI hinges heavily on reliable and accurate data. All the aforementioned use cases necessitate:
- Efficiently and easily storing and organizing continuously generated data from disparate sources spread across edge and cloud.
- Training machine learning models in an iterative fashion using the chosen modalities of data to enhance accuracy with the latest data.
- Integrating with labeling and curation frameworks in-house or utilizing third-party vendors, as the data often requires annotations.
- Ultimately, generating valuable insights or creating relevant datasets leveraging product and vector search capabilities, which, in turn, demand consistent indexing and continuous enrichment of all the data.
Next Steps For Your Multimodal AI Journey
Efficiently searching, accessing, processing, and visualizing data for reasons explained above, is crucial for AI success. Many companies initially opt for cloud-based storage but later realize that, especially for multimodal data like images, videos, and documents, relying solely on file names is woefully inadequate. Searching across various modalities necessitates multiple databases, each for metadata, labels, and embeddings. Preprocessing data into the right format involves complex libraries like ffmpeg or opencv. Stitching together these diverse data components is labor-intensive, suboptimal, and falls short of the needs of effective industrial and visual inspection.
Effective visual and industrial inspection requires a purpose-built multimodal data solution that establishes a central repository of multimodal data and attribute metadata, as well as track corresponding annotations, embeddings, datasets, and model behaviors. Such a database facilitates management of data from disparate sources and collaboration among teams that foster continuous improvement of managed information. This results in new operational insights, enhancing quality, and operational efficiency.
Consider ApertureDB - A Purpose-built Database For Multimodal AI
A unified approach to multimodal data, ApertureDB replaces the manual integration of multiple systems to achieve multimodal search and access. It seamlessly manages images, videos, embeddings, and associated metadata, including annotations, merging the capabilities of a vector database, intelligence graph, and multimodal data.
Navigate all images showing the "unfused" defect type, graphically, on ApertureDB UI
ApertureDB ensures cloud-agnostic integration with existing and new analytics pipelines, enhancing speed, agility, and productivity for data science and ML teams. ApertureDB enables efficient retrieval by co-locating relevant data and handles complex queries transactionally.
Use the ApertureDB client package on Jupyterlab to search for data by metadata or similarity.
Whether your organization has a small or large team working with multimodal data, or if you're simply curious about our technology and infrastructure development, reach out to us at team@aperturedata.io. Experience ApertureDB on pre-loaded datasets, and if you're eager to contribute to an early-stage startup, we're hiring. Stay informed about our journey and learn more about the components mentioned above by subscribing to our blog.
I want to acknowledge Laura Horvath for helping write this blog and the insights from Josh Stoddard, and the ApertureData team.
