Abstract
This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included.
Highlights
The concept of smart city (SC) was coined more than twenty years ago [1], nowadays it has a wide range of semantic interpretations and covers different meanings, which include many viewpoints of professionals and institutions involved [2]
In order to compare our approach with the results presented by Dev et al [21], we have included some additional average performance semantic segmentation metrics to evaluate the accuracy of detections for Single Shot MultiBox Detector (SSD) and YOLOv3 networks
This subsection summarizes the quantitative and qualitative results achieved in our dataset by the two detection deep networks which are compared: SSD and YOLOv3, respectively
Summary
The concept of smart city (SC) was coined more than twenty years ago [1], nowadays it has a wide range of semantic interpretations and covers different meanings, which include many viewpoints of professionals and institutions involved [2]. A SC is considered as an urban space where Information and Communication Technologies (ICT) are intensively applied to improve the quality and performance of urban services such as transportation, energy, water, infrastructures and other services (e.g., public safety) in order to reduce resource energy consumption, wastage and overall costs. The application of the best strategies, resources and available technologies to the SC environments will continuously improve the quality of life of their citizens and the operational efficiency of these complex urban systems. Out-of-home ( called outdoor) advertising continues to be very effective nowadays. The deployment and maintenance of such publicity infrastructures (including their support platforms) need funds from city governments, which are mainly paid by commercial brands in order to make more visible
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