Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling

Sambal Shikhar, Anupam Sobti; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5440-5449

Detecting various types of stresses (nutritional water nitrogen etc.) in agricultural fields is critical for farmers to ensure maximum productivity. However stresses show up in different shapes and sizes across different crop types and varieties. Hence this is posed as an anomaly detection task in agricultural images. Accurate anomaly detection in agricultural UAV images is vital for early identification of field irregularities. Traditional supervised learning faces challenges in adapting to diverse anomalies necessitating extensive annotated data. In this work we overcome this limitation with self-supervised learning using a masked image modeling approach. Masked Autoencoders (MAE) extract meaningful normal features from unlabeled image samples which produces high reconstruction error for the abnormal pixels during reconstruction. To remove the need of using only "normal" data while training we use an anomaly suppression loss mechanism that effectively minimizes the reconstruction of anomalous pixels and allows the model to learn anomalous areas without explicitly separating "normal" images for training. Evaluation on the Agriculture-Vision data challenge shows a 6.3% mIOU score improvement in comparison to prior state of the art in unsupervised and self-supervised methods. A single model generalizes across all the anomaly categories in the Agri-Vision Challenge Dataset.