The intention of this paper is to research on road damage detection and classification from road surface images using object detection method. This paper applied multiple convolutional neural network (CNN) algorithm to classify road damage and discovered which algorithm performs better in road damage ...
In each image, the bounding box representing the location of the damage and the type of damage are annotated. Next, we use the state-of-the-art object detection method using convolutional neural networks to train the damage detection model with our dataset, and compare the accuracy and run...
Next, we used state‐of‐the‐art object detection methods using convolutional neural networks to train the damage detection model with our data set, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can...
Road damage detection using deep ensemble learning. In: IEEE International Conference on Big Data (Big Data). 2020. Drozdal J, Weisz J, Wang D, Dass G, Yao B, Zhao C, Muller M, Ju L, Su H. Trust in AutoML: exploring information needs for establishing trust in automated machine ...
Next, we used state﹐f‐the゛rt object detection methods using convolutional neural networks to train the damage detection model with our data set, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can ...
Road manhole covers are crucial components of urban infrastructure; however, inadequate maintenance or poor marking can pose safety risks to vehicular traffic. This paper presents a method for detecting road manhole covers using a stereo depth camera and
arcgis.learn provides the SingleShotDetector (SSD) model for object detection tasks, which is based on a pretrained convnet, like ResNet that acts as the 'backbone'. More details about SSD can be found here. We will use the SingleShotDetector to train the damage detection model with backb...
Road damage detection is an important task to ensure road safety and realize the timely repair of road damage. The previous manual detection methods are low in efficiency and high in cost. To solve this problem, an improved YOLOv5 road damage detection a
Road damage is a great threat to the service life and safety of roads, and the early detection of pavement damage can facilitate maintenance and repair. Street view images serve as a new solution for the monitoring of pavement damage due to their wide co
image enhancement techniques and speeds of various object detection models. The models based on ResNet and VGG used in [1] converged at a faster rate, with higher mAP than models based on DenseNet. The false positives found were where the model showed a shadow detected as road dama...