## 2 Materials and Methods
### Automated Assessment of Potholes and Speed Bumps Using Imaging Processing  
The inception of the motivation for this research paper was induced directly or indirectly by our personal first-hand experiences with the Indian road conditions. The validation was for the determination of the efficiency of the proposed system. Initially, the pothole and bumps were detected \begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Hardware Description** \\ \hline Arduino & Microcontroller board based on ATmega328 using an open-source, prototyping platform, having 14 digital input/output pins (6 can be used as PWM outputs), 6 analog inputs, 16 MHz crystal oscillator, USB connection, power jack, ICSP header, and a reset button. \\  & Narrow acceptance angle, 5-pin male header with 2.54 mm (0.1\({}^{\prime}\)) spacing, 35 mA active, Range: approximately to 4 (12- feet), Power requirements: +5 VDC; Communication: Dimensions: \(22\times 46\times 16\) mm (0.81 \(\times\) 1.8 \(\times\) 0.6 in), positive TTL pulse, Operating temperature range: 0 to +70 \({}^{\circ}\)C (+32 to +158 \({}^{\circ}\)F). Figure 3(a) Arduino with HY-SRF05. \\ GPS Receiver & It is a satellite navigation system and is used to capture geographic location and time, irrespective of the weather conditions (Figure 3(b)). \\ HUD & A mobile device that used to display the information on the windshield as shown in Figure 3(c). \\ Filters & Several filters like Speed, Separating Z-Axis, Moving Average, Band Pass, and Virtual Orientation are used. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The hardware specification of the proposed framework. Figure 4: Components used in the road surface monitoring system: (**a**) Arduino with HY-SRF05; (**b**) GPS Receiver; (**c**) Head-up display module. with the help of using ultrasonic sensors paired with an Arduino attached with a heads-up display feeding the information to the GPS receiver. Irregularity frame selection is a recursive algorithm for searching all the vertices of a graph or tree data structure. This algorithm uses raw visual clips that are further put through several image processing methods; this is done using user-defined logics, which ensures the precise detection of irregularities. Segmentation of the irregular pixels in a visual frame is done by the algorithm using user defined decision logics that categorize those frames by the area covered by the irregular pixel. This segmentation is categorized into two parts: (1) Frames with irregularities, (2) frames without irregularities. The Hanuman algorithm is used in the presented technique for computerized location and estimation of speed bumps and potholes in a single go from a grouping of video outlines. This algorithm is generated by the arrangement of two visual properties of bumps and potholes, which are distinguished from video photographs of Indian streets. This aggregate arrangement of recognized visual properties incorporates the accompanying, including the following:  
**Picture Texture**: The picture surface of the interior of a pothole and speed bump is more different and distinguishing than the encompassing zone (i.e., irregularity less street surface region). **Shape Factor**: The state of a pothole and speed bump is distinguishable. Their shape factor is estimated as far as circularity, which changes depending on their surface territory and border. **Measurement**: The element of pothole and speed bump is expressed in terms of average width. This particular algorithm fundamentally includes five segments:  
* Image augmentation (Algorithm steps 3 and 4),
* Photo categorization (Algorithm step 5),
* Extraction of visual properties of articles (Algorithm steps 6 to 11),
* Recognition and classification of irregularities by decision logic (Algorithm step 12) and
* Quantification (Algorithm step 13). The calculation produced for the robotized identification, estimation, and arrangement of casings with potholes and speed bumps without basic irregularity from a grouping of street video outlines is recorded underneath. 1.