Digital image processing serves as a multifunctional tool for measurement and positioning tasks in robotics. The present paper deals with the development of a camera-based positioning system for quadrocopters in order to automate their landing process. In this regard, a quadrocopter equipped with classical radio-control components is upgraded with applicable hardware, such as a Raspberry Pi 3B+ and a wide-angle camera. Hereupon, black-box system identifications are executed to attain the relevant plants of the attitude control performed by the flight controller. Thereby, a PID-controller for the altitude control including a back-calculation anti-windup as well as two PD-controllers for the horizontal plane are designed using a pole placement method. Effective tests of the controller gains are conducted by simulating the closed loops respectively. Since the camera functions as a position sensor, an image processing algorithm is then implemented to detect a distinctive landing symbol in real time while converting its image position into compliant feedback errors (pixel-to-physical distance-conversion). Ultimately, the developed system allows for the robust detection and successful landing on the landing spot by a position control operating at 20 hertz.

Many complicated road intersections are seen while driving. In some, blind spots make it difficult for drivers or automated vehicles to discern moving objects coming from certain directions, possibly confusing drivers or autonomous vehicles wishing to cross or to turn at the intersection. To address this problem, we investigate detection and tracking of all moving objects at an intersection using a single 360-degree-view camera (3DVC). Through experiments, we develop methods allowing a 3DVC to capture the entirety of a four-way intersection when installed at one corner. This paper also presents image processing algorithms for detecting and tracking moving objects at intersections by processing images from the installed 3DVC. Experiments under varied conditions demonstrate that the proposed detection algorithm has a very high detection rate. We also confirm the tracking ability for moving objects detected using the proposed algorithm.

This paper describes a human search system that uses an unmanned aerial vehicle (UAV). The use of robots to search for people is expected to become an auxiliary tool for saving lives during a disaster. In particular, because UAVs can collect information from the air, there has been much research into human search using UAVs equipped with cameras. However, the disadvantage of cameras is that they struggle to detect people who are hidden in shadows. To solve this problem, we mounted an array microphone on a UAV and to detect the human voice as a means of finding people that cameras cannot. Also a search method is proposed that combines voice and camera human detection to compensate for their respective shortcomings. The rate and accuracy of human detection by the proposed method are assessed experimentally.

Recent development of deep convolutional neural networks (DCNN) devoted in creating a slim model for devices with lower specification such as embedded, mobile hardware, or microcomputer. Slim model can be achieved by minimizing computational complexity which theoretically will make processing time faster. Therefore, our focus is to build an architecture with minimum floating-point operation per second (FLOPs). In this work, we propose a small and slim architecture which later will be compared to state-of-the-art models. This architecture will be implemented into two models which are CustomNet and CustomNet2. Each of these models implements 3 convolutional blocks which reduce the computational complexity while maintains its accuracy and able to compete with state-of-the-art DCNN models. These models will be trained using ImageNet, CIFAR 10, CIFAR 100 and other datasets. The result will be compared based on accuracy, complexity, size, processing time, and trainable parameter. From the result, we found that one of our models which is CustomNet2, is better than MobileNet, MobileNet-v2, DenseNet, NASNetMobile in accuracy, trainable parameter, and complexity. For future implementation, this architecture can be adapted using region based DCNN for multiple object detection.

In this research, we present a system that enables independent walking in individuals with visual impairment by recognizing tactile paving. The system warns the user about dangers posed by deviations from the walking path or possible collisions. Therefore, the tactile paving itself and obstacles on it must be detected. This paper discusses problems related to detecting tactile paving. To address these problems, a new camera-based method is introduced, which detects tactile paving automatically. The method relies on a dynamic threshold approach in the HSV color space, making the image processing more robust against varying lighting conditions, environments, and differences in color. Such an approach is not possible using the methods based on fixed thresholds reported in the literature. The results of our experiments confirm that high rates of tactile paving detection were achieved under various conditions.

Visually impaired individuals worldwide are at a risk of accidents while walking. In particular, falling from a raised place, such as stairs, can lead to serious injury. Therefore, we attempted to determine the best accident prevention method that can notify visually impaired individuals of the existence, height, and step information when they approach stairs. In this study, we have investigated stair detection through deep learning. First, the three-dimensional point cloud data generated from depth information are learned by deep learning. Stairs were detected using the results of deep learning. To apply the point cloud data for deep learning-based training, we proposed preprocessing stages to reduce the weight of the point cloud data. The accuracy of stair detection was 97.3%, which is the best performance compared to other conventional methods. Therefore, we confirmed the effectiveness of the proposed method.