Automatic Signboard Detection and Localization in Densely Populated Developing Cities

TL;DR

This paper introduces a novel Faster R-CNN based method for automatic signboard detection in natural scene images, achieving high accuracy in densely populated developing cities with diverse signboard appearances.

Contribution

The paper proposes a specialized object detection approach with pretraining and hyperparameter optimization tailored for signboard localization in challenging urban environments.

Findings

Achieved 0.90 mAP on SVSO dataset.

Outperformed baseline methods in diverse urban scenes.

Demonstrated robustness across multiple developing countries.

Abstract

Most city establishments of developing cities are digitally unlabeled because of the lack of automatic annotation systems. Hence location and trajectory services such as Google Maps, Uber etc remain underutilized in such cities. Accurate signboard detection in natural scene images is the foremost task for error-free information retrieval from such city streets. Yet, developing accurate signboard localization system is still an unresolved challenge because of its diverse appearances that include textual images and perplexing backgrounds. We present a novel object detection approach that can detect signboards automatically and is suitable for such cities. We use Faster R-CNN based localization by incorporating two specialized pretraining methods and a run time efficient hyperparameter value selection algorithm. We have taken an incremental approach in reaching our final proposed method through detailed evaluation and comparison with baselines using our constructed SVSO (Street View Signboard Objects) signboard dataset containing signboard natural scene images of six developing countries. We demonstrate state-of-the-art performance of our proposed method on both SVSO dataset and Open Image Dataset. Our proposed method can detect signboards accurately (even if the images contain multiple signboards with diverse shapes and colours in a noisy background) achieving 0.90 mAP (mean average precision) score on SVSO independent test set. Our implementation is available at: https://github.com/sadrultoaha/Signboard-Detection