Compressed convolutional neural networks for sewer inspection

Abstract

The inspection of extensive and hard-to-access sewer systems is a challenging and expensive task. As these networks age and need to comply with stricter health and environmental regulations, the demand for effective inspection solutions has increased. The introduction of technologies like CCTV (closed-circuit television) and SSET (sewer scanner and evaluation technology) marked the initial automation steps in sewer inspections. Initially, images from these technologies were manually analyzed for defects, but over time, computer vision techniques have emerged as a highly promising method for automating image processing. However, these advanced computer vision methods are computationally intensive and typically rely on cloud-based architectures, which can be costly and sometimes impractical due to energy or communication limitations. A proposed solution is to shift the computational processes from the cloud to edge computing, which can address issues related to latency and scalability.

The Sewer-ML dataset, sourced from sewer inspection videos by Danish water utilities, comprises 1.3 million images annotated across 18 defect categories. This extensive multi-label dataset serves as a foundation for training and evaluating machine learning models within sewer system management. Model performance is evaluated using the $F2_{CIW}$ (class importance weight) score, which emphasizes recall and defect severity to ensure the accurate detection of critical defects, and the $F1_{Normal}$ score, which measures the model’s accuracy in identifying instances without defects, essential for efficient resource management.

The ResNet-101 and TResNet-L models, trained on the Sewer-ML dataset, underwent various compression methods including quantization, layer fusion, and pruning. Quantization was applied only to the ResNet-101, reducing its $F2_{CIW}$ and $F1_{Normal}$ scores by 2.52\% and 0.72\% respectively, while significantly boosting inference speed by up to 95\% on standard platforms and 174.50\% on L4 GPUs. Layer fusion was also implemented, further enhancing inference efficiency. Additionally, iterative pruning was performed, showing that while the TResNet-L could maintain performance up to an 80\% pruning rate, there was a noticeable initial drop in performance for both models.

The quantized ResNet-101, both the one with and without layer fusion,
even improve compared to the standard model in regards to correctly identifying
the highest CIW defect class present in pipes deemed defective in the validation
dataset. This model behaviour is positive because the priority of a sewer asset manager is the discovery of the defect that carry the highest risk with them if not treated
in time. This improved efficiency in defect recognition helps in optimizing repair
schedules and resource allocation, thus reducing operational costs as well.