Target detection is the basic technology of automatic driving system. Deep learning has gradually become the mainstream target detection algorithm because of its powerful feature extraction ability and adaptive ability. How to ensure accuracy and speed is a great challenge in the field of target detection. In order to solve the problems of high miss detection rate of small target and difficult to realize embedded real-time detection in the process of complex environment detection by deep learning method, this paper adds two auxiliary remaining network blocks in the backbone network. So that the backbone network can extract the global and local features of the detected object, and carry out feature extraction based on the feature pyramid network Fusion, adding a scale to form a three scale prediction, to improve the problem of poor detection accuracy of yolov4-tiny network. The simulation results show that: Compared with yolov4-tiny, the accuracy of the improved network structure is improved by 3.3%, and the detection speed is 251 fps, which ensures the requirements of real-time detection. This algorithm has good detection effect in the case of lack of illumination and target occlusion, and its detection accuracy on the mixed data set is better than that of the contrast algorithm, which meets the real-time detection conditions and is suitable for deployment on the embedded system carried by the car.
In the past ten years, multimodal image registration technology has been continuously developed, and a large number of researchers have paid attention to the problem of infrared and visible image registration. Due to the differences in grayscale distribution, resolution and viewpoint between two images, most of the existing infrared and visible image registration methods are still insufficient in accuracy. To solve such problems, we propose a new robust and accurate infrared and visible image registration method. For the purpose of generating more robust feature descriptors, we propose to generate feature descriptors using a concentric-circle-based feature-description algorithm. The method enhances the description of the main direction of feature points by introducing centroids, and, at the same time, uses concentric circles to ensure the rotation invariance of feature descriptors. To match feature points quickly and accurately, we propose a multi-level feature-matching algorithm using improved offset consistency for matching feature points. We redesigned the matching algorithm based on the offset consistency principle. The comparison experiments with several other state-of-the-art registration methods in CVC and homemade datasets show that our proposed method has significant advantages in both feature-point localization accuracy and correct matching rate.
Infrared pedestrian target detection is affected by factors such as the low resolution and contrast of infrared pedestrian images, as well as the complexity of the background and the presence of multiple targets occluding each other, resulting in indistinct target features. To address these issues, this paper proposes a method to enhance the accuracy of pedestrian target detection by employing contour information to guide multi-scale feature detection. This involves analyzing the shapes and edges of the targets in infrared images at different scales to more accurately identify and differentiate them from the background and other targets. First, we propose a preprocessing method to suppress background interference and extract color information from visible images. Second, we propose an information fusion residual block combining a U-shaped structure and residual connection to form a feature extraction network. Then, we propose an attention mechanism based on a contour information-guided approach to guide the network to extract the depth features of pedestrian targets. Finally, we use the clustering method of mIoU to generate anchor frame sizes applicable to the KAIST pedestrian dataset and propose a hybrid loss function to enhance the network’s adaptability to pedestrian targets. The extensive experimental results show that the method proposed in this paper outperforms other comparative algorithms in pedestrian detection, proving its superiority.
Accounting for the problems of dynamic infrared point targets are difficult to detect accurately and to track in complex background, the single-frame image background suppression by the high-pass filtering and binary processing. Then it uses unscented Kalman filter (UKF) method to get the predicted target trajectory and achieves a precise target tracking. According to the proposed detection and tracking algorithm of motion infrared point target, this paper designs and implements a system of high speed real-time image processing platform based on DSP+FPGA structure. Test results show that when the target is in the interference condition, tracking can still be stabilized, its processing time is about 9.2ms per frame to meet real-time requirements.
Linear canonical transformation is a new signal processing tools developing in recent years. As a unified multi-parameter linear integral transform, linear canonical transformation has its unique advantages when dealing with non-stationary signal. However, from the existing literatures, the basic theoretical system is not perfect, some of the theories associated with signal processing needs to be further established or strengthened, the research of linear canonical transformation has important theoretical significance and practical significance, but linear canonical transformation needs a lot of calculation, it is not like Fourier transform, fractional Fourier transform, Fresnel transform and scale operator, they have already been widely used in various fields of expertise, in order to reduce the amount of calculation, this paper puts forward a fast algorithm which uses duality theorem of linear canonical transformation to reduce the amount of calculation, it can quickly complete the operation when we use linear canonical transformation to process the signal during radar signal processing, the time for normal algorithm is 5s, the fast algorithm needs only 0.2s.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.