Deep learning has found extensive applications in the domain of autonomous driving. However, in complex road environments, diverse obstacles such as irregularly shaped objects, children's toys, animals, and other unconventional entities pose significant challenges. Convolutional Neural Network (CNN)-based road detectors struggle to satisfy real-time demands owing to the complexities associated with accommodating multi-scale and intricate backgrounds. In this paper, for the road obstacle detection problem in the field of autonomous driving, we propose a YOLOv8-based detection method, ROD-YOLO (Road Obstacle Detection), which has a better multi-scale adaptability, and the model is used to segment the obstacles on the road. Compared to the original network, ROD-YOLO adds a detection header, and this paper proposes a method to add Transfomer with GAM attention mechanism to the C2f module. In order to make the model better adapt to multi-scale obstacles, we add a new small-scale segmentation header and a special feature fusion part. Specifically the new GlobalCSP C2FGAM module is proposed with the C2STR module that incorporates the Transfomer idea to obtain faster segmentation speed and better accuracy for different obstacles, and the algorithm performs well in real-time object segmentation tasks and is able to maintain a high level of accuracy. It improves the mAP by 1.9% compared to the original network YOLOv8, which significantly improves the segmentation of small object samples. The research results in this paper are of great significance for improving the safety and efficiency of self-driving vehicles.
KEYWORDS: Control systems, Unmanned aerial vehicles, Process control, Telecommunications, Cameras, Video, Design and modelling, Signal processing, Sensors, Information technology
For the requirements of Unmanned Aerial Vehicles (UAVs) in high payload and small space, the aircraft needs to have the characteristics of high payload, small size and stable control system. This paper designs a coaxial twin-propeller multi-rotor UAV based on the combined control of redundant flight controller and NVIDIA microcomputer. Compared with traditional quadcopter UAVs, each axis is equipped with dual motors and dual propellers in this structure, which not only has high load-bearing capacity but also greatly reduces the occupied space of the aircraft. Through actual tests, it is found that the failure rate is almost 0 with the support of redundant flight controller, and the combined control system can hover stably and locate accurately under harsh conditions. Compared with the traditional flight control system, the system has better maneuverability and lower error offset when the UAV is under high payloads, making it more suitable for the aircraft with coaxial double-propeller multi-rotor structure.
Current tampering detection methods pay more attention to natural content images. The research on tampering algorithms for certificate document images is relatively limited, but certificate document images are the most commonly tampered with images, and they cause great harm to society. Our work presents a method for detecting certificate-like image manipulation using the ASGC-Net network. To achieve a network that can better localize text tampering cues. In addition, we propose a spatially constrained convolution that can effectively suppress image content and learn manipulation detection features by capturing different features between the neighborhood and the center of the convolution space. To increase the network's ability to capture tampering cues at multiple scales of images, we add multilayer cross-scale connections inspired by FPN networks. Experiments show that the algorithm is more accurate than general-purpose manipulation detection algorithms in locating tampered regions of certificate document images.
Current tampering detection methods pay more attention to natural content images. The research on tampering algorithms for certificate document images is relatively limited, but certificate document images are the most commonly tampered with images, and they cause great harm to society. Our work presents a method for detecting certificate-like image manipulation using the ASGC-Net network. To achieve a network that can better localize text tampering cues. In addition, we propose a spatially constrained convolution that can effectively suppress image content and learn manipulation detection features by capturing different features between the neighborhood and the center of the convolution space. To increase the network's ability to capture tampering cues at multiple scales of images, we add multilayer cross-scale connections inspired by FPN networks. Experiments show that the algorithm is more accurate than general-purpose manipulation detection algorithms in locating tampered regions of certificate document images.
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