Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutions
Visual Anomaly Detection (VAD) has emerged as a pivotal technology in a variety of fields, including industrial inspection and healthcare. The ability to detect anomalies in visual data is crucial for maintaining quality and ensuring safety. Despite the considerable progress made in this area, two significant challenges remain largely unaddressed: the constraints of edge deployment and the need for continual learning.
Edge deployment refers to the deployment of models in environments where computational resources are limited. This is particularly relevant in applications where real-time processing is essential. On the other hand, continual learning necessitates that models adapt to new data distributions over time without losing the knowledge acquired from previous data. These two challenges, when combined, create a complex environment that existing methods struggle to navigate effectively.
Benchmarking VAD on Edge Devices
Our research introduces the first comprehensive benchmark specifically designed for VAD on edge devices within a continual learning framework. This benchmark serves as a vital resource for practitioners and researchers in the field, offering guidance on selecting the most suitable backbone and VAD method while considering both efficiency and adaptability constraints.
- Memory Footprint: The amount of memory required to deploy the model.
- Inference Cost: The computational resources needed to make predictions.
- Detection Performance: The accuracy and reliability of the anomaly detection process.
By systematically evaluating these factors, our benchmark characterizes the trade-offs that must be made when deploying VAD solutions in constrained environments. It is essential to recognize that studying these challenges in isolation leads to misleading conclusions, as solutions tailored for one constraint often make assumptions that fail under the imposition of the other.
Introducing Tiny-Dinomaly
In addition to our benchmark, we propose a novel solution called Tiny-Dinomaly. This model is a lightweight adaptation of the Dinomaly framework, constructed on the foundation of the DINO model. Tiny-Dinomaly demonstrates impressive efficiency, achieving a 13-fold reduction in memory footprint and a 20-fold decrease in computational cost compared to its predecessors. Notably, it also enhances Pixel F1 scores by an impressive 5 percentage points, illustrating that efficiency does not have to come at the expense of performance.
Improving Existing Models
To further advance the field, we have introduced targeted modifications to existing VAD models, specifically PatchCore and PaDiM. These adaptations are designed to enhance their efficiency in the context of continual learning, ensuring that they remain effective as they adapt to new data distributions over time.
Conclusion
The challenges of deploying Visual Anomaly Detection models on edge devices while facilitating continual learning are significant but not insurmountable. Through our comprehensive benchmark and innovative solutions such as Tiny-Dinomaly, we provide valuable insights and tools for researchers and practitioners. As the field continues to evolve, addressing these challenges will be crucial for the future of VAD, enabling more effective and efficient applications across various industries.