Assessment of deep learning-based image analysis for disaster waste identification

Abstract

Accurately predicting different types and quantities of wastes is crucial for proper waste management and sustainable growth and development. High levels of disaster waste (DW) can negatively impact emergency responses, public health, and recovery and rebuilding processes, making the effective management of DW highly important. Most existing waste identification methods cannot suitably identify DW since it is highly subjective in form. In this study, we examined the applicability of different deep learning-based image recognition techniques for DW recognition. A DW image dataset was collected and categorized into normal and hard cases. Then, experiments were conducted using these three network models: DeepLabV3+, ResNeSt-50, and ResNeSt-101. The experimental results indicated that the mean intersection over union (mIoU) of the DeepLabV3+ model was 0.802 for the normal case and that of the ResNeSt-101 model was 0.676 for the hard case, indicating favorable performances. Overall, the deep learning-based image analysis method is more robust than existing methods, particularly in terms of its ability to accurately identify waste in various forms, such as mixed or piled up waste.