# Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera
### Related Works Using Multi-Dimensional Signals  
In order to design more accurate and reliable top detection systems, more sensors have been added to detect them, such as lidar, radar, and video cameras. The study in reference [14] presents an environment mapping and sensing method for real-time autonomous driving for rural and off-road environments. This has been designed in two parts: (1) camera system to detect lanes, pedestrian crossings, and speed bumps; and (2) obstacle detection system using lidar. The first part returns lane positions using the vision module \"VisLab Embedded Lane Detector (VELD)\". This prototype achieved an accuracy of 85%. In reference [17], [PERSON] et al., report detection of speed bumps with 85% accuracy, either to alert or to directly interact with the vehicle. It was made with the help of image processing concepts. The methodology uses the smartphone's hardware such as GPS. This procedure is designed for roads built with proper signage. In 2016, [PERSON] et al. [18] proposed an Intelligent Transportation System (ITS) for traffic and transportation management for smart and safe driving. The Advanced Driver Assistance System (ADAS) belongs to ITS, which provides alerts, warnings, or information to the user while driving. The proposed system detects speed bumps but does not detect low speed bumps, which causes misidentification in several cases. The proposed method uses Gaussian filtering and median filtering to remove noise in the image. Subsequently, image subtraction is achieved by subtracting the median filtered image from the Gaussian filtered image. The resulting image is converted to a binary image, and the regions are analyzed using the connected component approach. The system works for bumpers with suitable paint regardless of their dimension, and it achieved 92% accuracy. In other research [19], [PERSON] et al. present a speed bump detection method based on the Gaussian mixture model. The method based on the speed bump stripe pattern is applied to segment it from the highway and several other environments. The morphological closure operator is then performed to complete the speed bump area. In the results, both daytime and nighttime conditions were tested. The experimental results show that the proposed system can detect the protuberances with an efficiency of 94.7%. [PERSON] et al. [8] propose a method that detects and informs the driver about the upcoming unmarked and marked speed bumps/humps in real time using deep learning techniques and provide the distance the vehicle is using stereo vision approaches. It was built using NVIDIA GPUs and Stereolabs ZED Stereo camera hardware. The driver or autonomous system of the vehicle can control the speed of the vehicle to be in safer limits in order not to cause turbulence to the passengers or damage to the vehicle. The accuracy of the system achieves 97.4%. [PERSON] et al. [4] presented an algorithm for detecting and describing potholes and bumps from noisy signals acquired using an accelerometer. A wavelet transformation filter was used to analyze the signals into multiple scales. Its coefficients were correlated across near scales and filtered using a spatial filter. Road anomalies were detected on a fixed threshold system, while characterization was achieved using unique features extracted from the filtered wavelet coefficients. Their analyses show that the proposed algorithm detects and characterizes road anomalies with 94-96% of accuracy. [PERSON] et al. [20] proposed a speed breaker identification method using gray-level co-occurrence matrix (GLCM) features. This approach has three stages: pre-processing, feature extraction, and classification. Noise removal, rescaling the image, and gray scale conversion has been constructed as a part of pre-processing. In the feature extraction stage, the pixels' spatial relationship is obtained. The image's second order statistical GLCM are used as features. These characteristics include correlation, angular second moment, entropy, homogeneity, and contrast.