## 1 Introduction
[11] used a global positioning system receiver and ultrasonic for the identification of geographical location coordinates of the detected potholes and speed breaker with 59.23% efficiency. [PERSON] et al. [12] worked on the automatic detection and notification of potholes and humps on roads to aid drivers using ultrasonic and a Global Positioning System (GPS). It was found that the system was 74% efficient and the data could be stored in the cloud. [PERSON] et al. [13] also proposed an intelligent system using an ultrasonic sensor based on Raspberry Pi, GPS, and ultrasonic to take corrective measures. [PERSON] et al. [14] proposed a real-time system that detected speed bumps by the analysis of photos of the road that were further enhanced using a Gaussian Filter (computer vision technique). This system had a correct rate ranging from 30% to 92%; however, failure was witnessed for faded speed bumps, and for a higher price, they had to be painted and maintained; this was a significant drawback of the system. [PERSON] et al. [8] established the patrol system that comprised of a GPS and a 3-axis accelerometer for the detection of potholes. Likewise, [PERSON] et al. [15] used the very same system and evaluated the power spectral density (PSD) for the detection of pavement irregularities using Fourier transformations. Inaccurate location of the irregularities was a shortcoming of the system. Nericell was a system developed by [PERSON] et al. [7] that comprised of amici, global system for mobile communication (GSM), accelerometer, and GPS sensors for road surface monitoring. The method yielded a False Negative Rate (FNR) of 37% and 41% for low speed and high-speed bumps, respectively. The use of smartphones, an adaptive fuzzy classifier, GPS, and inertial sensors for the detection of sudden driving events was presented by [PERSON] et al. [16] and a precision performance of 0.87 and 0.91 was recorded. A fuzzy inference system was proposed by [PERSON] et al. [17] for the detection of speed bumps using accelerometers embedded in smartphones. Nevertheless, the actual results of the tests were missing and accuracy only ranged from 70-80%. A multi-mobile phone system was developed by [PERSON] et al. [18], who also used accelerometersfor the detection of bumps and potholes. A whopping 90% detection rate was obtained for bumps whereas the False Positive Rate (FPR) was about 35%. Furthermore, the use of a smartphone accelerometer with six different algorithms to categorize road surface conditions was done by [PERSON] et al. [19], who reported an Area Under Curve (AUC) ranging from 0.823 to 0.9445. Nature driven approaches such as genetic algorithm have also been explored: [PERSON] and [PERSON] [20] explored the use of genetic algorithms using images as a source of information to detect road irregularities. Moreover, abnormalities or distress data collection are increasingly being automated by using various imaging systems. However, analysis of the collected raw video clips for distress assessment is still predominantly being done manually, which is expensive, time-consuming, and slows down road maintenance management [21]. Pre-existing research suggests that three-dimensional image-based techniques are quite expensive; on the other hand, vibration sensor-based techniques are not as accurate and reliable [22] for road surface monitoring. The majority of the existing two-dimensional imaging methods have solely focused on either crack distress detection or pothole detection. In this paper, an experimental analysis was conducted to detect potholes and speed bumps in quasi-real-time conditions using three different techniques, namely, an ultrasonic sensor with DWT, imaging-based systems, and manual inspection for validation from various roads of Noida city. In the first technique, which consists of an ultrasonic sensor, a GPS and Arduino connected to the Head Up Display (HUD) device are introduced. This system is a cost-effective method for a better, faster, and more reliable way to avoid accidents caused by potholes and raised humps.