Current deep learning methods seldom consider the effects of small pedestrian ratios and considerable differences in the aspect ratio of input images, which results in low pedestrian detection performance. This study proposes the ratio-and-scale-aware YOLO (RSA-YOLO) method to solve the aforementioned problems. The following procedure is adopted in this method. First, ratio-aware mechanisms are introduced to dynamically adjust the input layer length and width hyperparameters of YOLOv3, thereby solving the problem of considerable differences in the aspect ratio. Second, intelligent splits are used to automatically and appropriately divide the original images into two local images. Ratio-aware YOLO (RA-YOLO) is iteratively performed on the two local images. Because the original and local images produce low- and high-resolution pedestrian detection information after RA-YOLO, respectively, this study proposes new scale-aware mechanisms in which multiresolution fusion is used to solve the problem of misdetection of remarkably small pedestrians in images. The experimental results indicate that the proposed method produces favorable results for images with extremely small objects and those with considerable differences in the aspect ratio. Compared with the original YOLOs (i.e., YOLOv2 and YOLOv3) and several state-of-the-art approaches, the proposed method demonstrated a superior performance for the VOC 2012 comp4, INRIA, and ETH databases in terms of the average precision, intersection over union, and lowest log-average miss rate.

In brain-computer interface (BCI) work, how correctly identifying various features and their corresponding actions from complex Electroencephalography (EEG) signals is a challenging technology. However, most current methods do not consider EEG feature information in spatial, temporal and spectral domains, and the structure of these models cannot effectively extract discriminative features, resulting in limited classification performance. To address this issue, we propose a novel text motor-imagery EEG discrimination method, namely wavelet-based temporal-spectral-attention correlation coefficient (WTS-CC), to simultaneously consider the features and their weighting in spatial, EEG-channel, temporal and spectral domains in this study. The initial Temporal Feature Extraction (iTFE) module extracts the initial important temporal features of MI EEG signals. The Deep EEG-Channel-attention (DEC) module is then proposed to automatically adjust the weight of each EEG channel according to its importance, thereby effectively enhancing more important EEG channels and suppressing less important EEG channels. Next, the Wavelet-based Temporal-Spectral-attention (WTS) module is proposed to obtain more significant discriminative features between different MI tasks by weighting features on two-dimensional time-frequency maps. Finally, a simple discrimination module is used for MI EEG discrimination. The experimental results indicate that the proposed text WTS-CC method can achieve promising discrimination performance that outperforms the state-of-the-art methods in terms of classification accuracy, Kappa coefficient, F1 score, and AUC on three public datasets.

Although pedestrian detection technology is constantly improving, pedestrian detection remains challenging because of the uncertainty and diversity of pedestrians in different scales and of occluded pedestrian modes. This study followed the common framework of single-shot object detection and proposed a divide-and-rule method to solve the aforementioned problems. The proposed model introduced a segmentation function that can split pedestrians who do not overlap in one image into two subimages. By using a network architecture, multiresolution adaptive fusion was performed on the output of all images and subimages to generate the final detection result. This study conducted an extensive evaluation of several challenging pedestrian detection data sets and finally proved the effectiveness of the proposed model. In particular, the proposed model achieved the most advanced performance on data sets from Visual Object Classes 2012 (VOC 2012), the French Institute for Research in Computer Science and Automation, and the Swiss Federal Institute of Technology in Zurich and obtained the most competitive results in a triple-width VOC 2012 experiment carefully designed by the present study.

Pedestrian detection remains a crucial technology for applications such as autonomous driving and gait recognition and continues to be a prominent research area. Despite the development of advanced pedestrian detection techniques, the challenge of detecting pedestrians in low-resolution images persists in real-life scenarios where low-quality imaging devices are still in use. The objective of this study is to enhance the detection of pedestrians in low-resolution (LR) images by improving image quality through super-resolution techniques. To achieve this goal, we propose an end-to-end Multi-scale Structure-Enhanced Super-Resolution (MsSE-SR) method to enlarge LR images into high-resolution (SR) images and utilize Yolov4 for detection, effectively addressing the issue of low detection performance in LR images. To generate an SR image that can accurately distinguish between foreground and background elements while emphasizing pedestrian characteristics, we employ the stationary wavelet transform (SWT) to decompose the image into low and high-frequency sub-images. These sub-images are then processed through different network structures, enabling the network to reconstruct high-frequency details and low-frequency structures with greater precision. Moreover, we propose a high-to-low subnetwork information transfer (H2LSnIT) that incorporates high-frequency edge information into the low-frequency image structure during the reconstruction process, leading to a more detailed reconstruction of the low-frequency structure. We also propose a novel loss function that leverages the characteristics of wavelet decomposition to enhance the network’s focus on reconstructing the image structure, further improving detection performance. The experimental results demonstrate the effectiveness of the proposed MsSE-SR method in significantly enhancing pedestrian detection performance.

Haze reduces the contrast of an image and causes the loss in colors, which has a negative effect on the subsequent object detection; therefore, single image dehazing is a challenging visual task. In addition, defects exist in previous existing dehazing approaches: Pixel-based dehazing approaches are likely to result in insufficient information to estimate the transmission, whereas patch-based ones are prone to generate shadows. They both also tend to induce color deviations. Therefore, this study proposes a novel method based on multi-scale wavelet and non-local dehazing. A hazy image is first decomposed into a low-frequency and three high-frequency sub-images by wavelet transform. Non-local dehazing and wavelet denoising are then employed on the low-frequency and high-frequency sub-images to remove the haze and noise, respectively. Finally, a haze-free image is obtained from the reconstruction of sub-images. The proposed method focuses on the dehazing and denoising on the low-frequency and high-frequency images respectively, through which the details on the image can be well preserved. Experimental results indicate that the proposed method is superior to the state-of-the-art approaches in both quantitative and qualitative evaluation on the synthetic and real-world image datasets.