Visual Detection - Damaged Containers in shipping process

Subject: Improvement in Visual Damage Detection for Shipment Containers

We are currently facing challenges in effectively detecting visual damage on our shipment containers as they travel from origin to destination. Our current method involves having one person at the origin and another at the destination complete surveys to compare lists, but human observations can be limited when it comes to identifying subtle issues. Considering that these containers carry expensive and perishable products requiring cold storage, it is crucial to enhance our damage detection process.

One potential solution worth exploring is leveraging OpenAI GPT-4o technology. By utilizing advanced AI models like GPT-4o, we may be able to improve the accuracy of identifying damaged containers throughout the shipment journey. However, a key consideration is whether these models can distinguish between good and damaged containers without specific training data on our custom containers.

If we go GPT-4o route, how we training the AI model with hundreds of visual examples of both intact and damaged custom containers to facilitate learning. Is this approach would involve providing sufficient data for the AI to recognize patterns associated with container damage specific to our products. It’s important to note that while OpenAI may not have pre-existing data on our custom containers, training the model with tailored visuals could potentially bridge this gap.

Your thoughts on this proposed strategy and any additional insights you may have would be greatly appreciated as we seek to enhance our visual damage detection processes for our specialized shipment containers.