X3D-based Illegal Waste Dumping Detection with Temporal Localization

Abstract

Illegal waste dumping poses significant environmental and public health challenges worldwide, requiring automated surveillance systems for detection and prevention. This paper presents our solution for the IWDD 2026 Contest, addressing the dual challenge of detecting illegal dumping events in surveillance videos and localizing the exact moment of occurrence. We employ X3D-M, an efficient 3D convolutional network pretrained on Kinetics-400, combined with a sliding window inference strategy for temporal localization. Through systematic hyperparameter optimization across 96 configurations and ablation studies examining nine combinations of fine-tuning strategies and loss functions, we identify key design choices for this application domain. Our experiments reveal that differential learning rates-applying lower rates to the pretrained backbone while training the classifier more aggressively-outperform both frozen backbones and uniform fine-tuning. The optimal system achieves an F1-score of 0.8387 and a temporal F1score of 0.7742 on our test set, with 92.3% of correct detections within the temporal tolerance window. Operating at over 8 times real-time speed with only 2.97M parameters, our approach demonstrates that efficient video classification architectures can be effectively adapted for specialized surveillance applications through careful transfer learning and inference design.