Object & Defect Detection In Dynamic Environment Conditions
Developing a robust inspection system to enhance automation and quality assurance in logistics under varying lighting conditions.
Problem
In logistics, particularly in goods reception, the manual spot-checking of incoming goods often results in missed defects and undetected damages. Containers, boxes, or even missing goods are frequently recognized only later in the process, leading to increased costs. Traditional inspection solutions struggle under varying lighting conditions commonly found in goods reception areas, making automated inspection difficult to implement.
Goal
The goal is to achieve 100% automated quality inspection of incoming goods during unloading, reducing manual labor and human error. By detecting defects and missing goods early in the process, the solution aims to significantly cut operational costs.
Solution
To address these challenges, synthetic data is leveraged for model training, ensuring a robust inspection system that can handle varying lighting conditions. The solution includes deriving best practices for hardware setup to avoid disruption to current processes.
Multiple images are captured from various angles and combined into a comprehensive inspection result. Different models are trained to detect both missing goods and damages on containers and boxes. Over time, inspection results are analyzed to continuously improve the system and adapt to new container types or product variants.
Customer Value
With our SightHub, the customer can now fully automate the detailed inspection of incoming goods, ensuring resilience against varying lighting conditions. By leveraging synthetic data, the system incorporates diverse lighting scenarios into the model development, creating a robust and flexible solution.
Additionally, the inspection application can easily adapt to new container types or products. Detecting defects and missing goods early in the process reduces manual effort and prevents costly delays later in the logistics chain, leading to significant cost savings.