Many datasets used to train artificial intelligence systems to recognize potholes, such as the challenging sequences for autonomous driving (CCSAD) and the Pacific Northwest road (PNW) datasets, do not produce satisfactory results. This is due to the fact that these datasets present complex but realistic scenarios of pothole detection tasks than popularly used datasets that achieve better results but do not effectively represents realistic pothole detection task. In remote sensing, super-resolution generative adversarial networks (GAN), such as enhanced super-resolution generative adversarial networks (ESRGAN), have been employed to mitigate the issues of small-object detection, which has shown remarkable performance in detecting small objects from low-quality images. Inspired by this success in remote sensing, we apply similar techniques with an ESRGAN super-resolution network to improve the image quality of road surfaces, and we use different object detection networks in the same pipeline to detect instances of potholes in the images. The architecture we propose consists of two main components: ESRGAN and a detection network. For the detection network, we employ both you only look once (YOLOv5) and EfficientDet networks. Comprehensive experiments on different pothole detection datasets show better performance for our method compared to similar state-of-the-art methods for pothole detection.

Although several recommendation algorithms are widely used by both commercial and non-commercial platforms, they face unique challenges such as sparse data sets and the absence of negative or “neutral” feedback. One-class algorithms attempt to overcome the data sparsity problem by using the implicit feedback inherent in user’s clicks and purchases, which are deduced from both positive and negative feedback. Existing literature uses several heuristic strategies to derive the negative samples needed for training, such as using random sampling or utilizing user-item interaction. However, these assumptions do not always reflect reality. In addition, with the explosive increase in the availability of big data for training recommendation systems, these methods might not adequately encapsulate the representations of the latent vectors. In this paper, we address the common issues of one-class recommendation and provide a survey on approaches that have been used to mitigate the existing challenges. To tackle the identified problems, we propose a neural network-based Bayesian Personalized Ranking (BPR) for item recommendation and personalized ranking from implicit feedback. BPR provides an optimization criterion derived from Bayesian analysis of a problem to develop an optimized model for such a problem. We conduct several experiments on two varieties of MovieLens datasets to illustrate the performance of the proposed approach. Our approach shows an impressive result in mitigating the issues of one-class recommendation when compared with the complexity of the state-of-the-art methods.