Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function

TL;DR

This paper introduces an adaptive weighted cross-entropy loss combined with Jaccard distance to improve the speed and accuracy of pixel-level road crack detection, addressing foreground-background imbalance effectively.

Contribution

It proposes a novel adaptive loss function that enhances crack detection performance and training efficiency compared to traditional methods.

Findings

Significantly faster training with maintained accuracy.

Effective handling of foreground-background imbalance.

Validated on four public crack detection datasets.

Abstract

Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground-background imbalance. Fortunately, modification of loss function appears to solve this puzzle once and for all. In this paper, we propose a pixel-based adaptive weighted cross-entropy loss in conjunction with Jaccard distance to facilitate high-quality pixel-level road crack detection. Our work profoundly demonstrates the influence of loss functions on detection outcomes, and sheds light on the sophisticated consecutive improvements in the realm of crack detection. Specifically, to verify the effectiveness of the proposed loss, we conduct extensive experiments on four public databases, i.e., CrackForest, AigleRN, Crack360, and BJN260. Compared with the vanilla weighted cross-entropy, the proposed loss significantly speeds up the training process while retaining the test accuracy.