Industrial Video Anomaly Detection Using a Weakly Supervised Predictive Autoencoder
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In this thesis, a predictive autoencoder model and video data pipeline are formulated
for detecting anomalies in production flow in industrial environments. Common
modifications to deep neural networks, such as spatial attention blocks, dropouts and
skip connections, are investigated to assess whether they affect the overall anomalydetection
performance for the intended industrial scenario. The project was done in
cooperation with the company EyeAtProduction AB in Borås, Sweden.
The model is designed for flexibility, robustness and short training times in new
environments, rather than state of the art performance. It uses a pretrained version
of the image recognition network ResNet-18 for encoding sequences of four video
frames. The encoded frames are merged with a 1 × 1 convolution operation, and
then decoded via transposed convolutions, resulting in a prediction of the next
frame following the sequence. By only training the network on footage of normal
production, it will become proficient at predicting normal movement and spatial
features, but struggle to reconstruct anomalous sequences and objects. Anomalies
can therefore be detected based on the degree of error between the prediction and
the true next frame.
The models show promise in both controlled environments and real-world cases, but
even with heavy data augmentation they are still sensitive to lighting changes and
vibrations in the camera, making them prone to false positives. More research would
need to be done to minimize this problem further, but possible solutions could be
collecting larger and more diverse training sets, and making the threshold adapt to
the long term shifts in the prediction scores during inference.
Beskrivning
Ämne/nyckelord
Anomaly detection, autoencoder, convolutional network, ResNet, production processes