Efficient Single-Shot Multibox Detector for Construction Site Monitoring

TL;DR

This paper enhances the Single-Shot Multibox Detector (SSD) for construction site monitoring by integrating Affinity Propagation Clustering to improve detection accuracy, demonstrating notable gains on custom and standard datasets.

Contribution

It introduces a novel clustering-based approach to improve SSD performance, moving beyond traditional non-maximum suppression techniques.

Findings

Improved mean average precision by 3.77% on custom construction dataset.

Achieved 1.67% improvement on PASCAL VOC dataset.

Demonstrated effectiveness of clustering in object detection enhancement.

Abstract

Asset monitoring in construction sites is an intricate, manually intensive task, that can highly benefit from automated solutions engineered using deep neural networks. We use Single-Shot Multibox Detector --- SSD, for its fine balance between speed and accuracy, to leverage ubiquitously available images and videos from the surveillance cameras on the construction sites and automate the monitoring tasks, hence enabling project managers to better track the performance and optimize the utilization of each resource. We propose to improve the performance of SSD by clustering the predicted boxes instead of a greedy approach like non-maximum suppression. We do so using Affinity Propagation Clustering --- APC to cluster the predicted boxes based on the similarity index computed using the spatial features as well as location of predicted boxes. In our attempts, we have been able to improve the mean average precision of SSD by 3.77% on custom dataset consist of images from construction sites and by 1.67% on PASCAL VOC Challenge.