  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.
