###### Abstract  
Owing to the low detection accuracy of camera-based object detection models, various fusion techniques with Light Detection and Ranging (LiDAR) have been attempted. This has resulted in improved detection of objects that are difficult to detect due to partial occlusion by obstacles or unclear silhouettes. However, the detection performance remains limited in low-light environments where small pedestrians are located far from the sensor or pedestrians have difficult-to-estimate shapes. This study proposes an object detection model that employs a Gaussian-Sobel filter. This filter combines Gaussian blurring, which suppresses the effects of noise, and a Sobel mask, which accentuates object features, to effectively utilize depth maps generated by LiDAR for object detection. The model performs independent pedestrian detection using the real-time object detection model You Only Look Once v4, based on RGB images obtained using a camera and depth maps preprocessed by the Gaussian-Sobel filter, and estimates the optimal pedestrian location using non-maximum suppression. This enables accurate pedestrian detection while maintaining a high detection accuracy even in low-light or external-noise environments, where object features and contours are not well defined. The test evaluation results demonstrated that the proposed method achieved at least 1-7% higher average precision than the state-of-the-art models under various environments. pedestrian detection; Gaussian-Sobel; depth map; low light; point cloud +
Footnote †: journal: Medical  
detection performance in occluded environments by suppressing feature extraction from object-free background images. [PERSON] et al. [7] proposed a single-image super-resolution network model based on CNNs, combining conventional autoencoders with residual neural network approaches. By removing the noise present in images, they improved the image quality and enhanced detection accuracy. [PERSON] et al. [8] also proposed a method to enhance image quality by directly embedding a physical lighting model into a deep neural network to improve object detection performance in low-light conditions. This method captures the difference between the local content of an object and the preferred region of its local neighborhood. However, these techniques exhibit shortcomings in object detection when parts of objects are obscured by other objects, or when noise induced by light reflection, scattering, and other low-light conditions results in the loss of some contour information. Given the limitations of camera-based object detection systems due to various external environmental factors such as low-light conditions, supplementary sensors have been incorporated into these systems to enhance their performance. [PERSON] et al. [9] enhanced pedestrian detection performance in low-light conditions by generating a depth map based on Light Detection and Ranging (LiDAR) and stereovision sensors, mitigating the effects of light noise. [PERSON] et al. [10] explored the use of CNNs to improve small object detection performance based on both three-dimensional (3D) point cloud data (PCD) and two-dimensional (2D) images. [PERSON] et al. [11] predicted the location of objects in 2D images based on CNNs and converted the corresponding regions into three dimensions, thus improving the detection performance. Although all the aforementioned studies demonstrated enhanced detection accuracy of invisible objects by employing multiple sensors, their performance in detecting small objects in low-light environments and at a large distance from the sensor remained suboptimal, primarily owing to the challenges associated with object shape estimation in such environments. Although LiDAR was additionally used, object detection in low-light scenarios continued to be challenging because of the low-resolution depth maps produced by the LiDAR, which hindered accurate object detection. Hence, to improve the performance, supplementary image processing is essential to highlight the features of an object.