## 5 Conclusions  
In this paper, we introduced the foundational block for implementing an acquisition system for a physical asset management tool. The detection system is geared road damage identification and makes use of a very recent generic object detector (RetinaNet) trained on a quality aware filtered dataset. The proposed method can detect various asphalt structural damages from video with high accuracy and low inference times, effectively serving as a stepping stone for implementing sophisticated road damage acquisition systems. We conducted a thorough comparison between various object detectors and several ablation studies, for both detectors based on traditional computer vision algorithms and others based on two- and one-stage objects detectors. For validating our models, we made use of a very large dataset of structural damages initially proposed in [25], which we augmented with road damage samples from Mexican and Italian roads [17]. The main rational for this approach was to mitigate the class imbalance present for some road damage types such as potholes and alligator cracks, which are not well represented in the previous dataset. Furthermore, we proposed and made use of an image quality aware data augmentation strategy for discarding road damage samples that could hamper the performance of the trained detectors, and we carried out an ablation study to demonstrate the effectiveness of such an approach for training deep learning-based object detectors. The ablation study clearly demonstrated that data augmentation is beneficial for training the generic object detectors, but its effectiveness can be hampered owing to the poor quality of the images, as documented in the literature. As demonstrated by our experiments, a simple, yet powerful \"image filtering\" technique can ameliorate the proceedings and lead to a more optimal model. Among the various tested models, the results demonstrated that RetinaNet outperforms other recent architectures for object detection, while producing a model that it is efficient for smartphone implementation (in terms of memory footprint) and attaining acceptable inference rates (500 ms in our RetinaNet model). An additional advantage of RetinaNet is that it presents less jitter in the detection, owing to improved non-maximum suppression strategies and leading to a better performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model/Metric** & **Memory Footprint** & **Latency** & **Best Accuracy** \\ \hline RetinaNet [22] & 115.7 MB & 500 ms & 0.8279 \\ MobileNet [24] & Not reported & 1500 ms & Not reported \\ RetinaNet (ours) & 125.5 MB & 500 ms & 0.91522 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Metrics comparison for our best model against the state of the art. The obtained inference times are lower than those in previous research (500 ms in contrast to 1500 ms in [24]); because the detector is to be implemented on mobile phones attached to a car, low inference times are of paramount importance, and although 500 ms might seem not sufficiently low, there is also a compromise to be made in terms of accuracy and memory footprinted, as discussed in Section 4.4. Nonetheless, previous research [25] demonstrated that less than 1500 ms is sufficient for imaging road damages for a vehicle moving at 40 km/s (10 m/s), while avoiding information duplication, as the line of sight is sufficiently large for most purposes. As a continuation of this research, we plan to introduce the generated detector and mobile application into a tool for prognosis and big data analysis applied to road asset management as a cloud service. The main goal is taking full advantage of the capabilities of a heterogeneous architecture for follow up operations in road asset management by municipal or regional organizations. If coupled with a data analytics and prognosis model, this geo-referenced road damage database could lead to a better decision-making process in road management, in both the allocation of resources for maintenance as well as the required periodicity.