###### Abstract
A sharpening filter presents a viable option for this enhancement. Additionally, the PCD generated by LiDAR is susceptible to particle-based external noise, which is another factor to be considered when choosing a sharpening filter. Over the years, researchers have explored various methods of image enhancement using sharpening filters. [PERSON] and [PERSON] [12] achieved image enhancement by expanding pixels at the presumed locations of objects in all directions through morphological operations, thereby highlighting object contours. The image quality was enhanced by centering the structural elements on the foreground pixels. If a foreground pixel was present in the area where the elements overlapped, the central pixel of that area was designated as the foreground pixel, thereby enlarging the size and outline of the objects. [PERSON] [13] enhanced the sharpness of objects by applying an unsharp mask filter that increases object density. This filter was created using a mask obtained from the difference between a blurred image and the original image, thereby increasing the contrast between pixels. [PERSON] and [PERSON] [14] utilized a Canny edge filter to identify pixels with the greatest rate of change as edges and employed hysteresis edge tracking to reduce noise effects and eliminate isolated edges, thereby enhancing the sharpness of object contours. The application of reconstructed images using these filters to object detection models is expected to lead to considerable performance improvements. However, object detection performance could be degraded instead if some object information is missing owing to image filtering or if nearby objects appear to be overlapped. Furthermore, the inadvertent amplification of external noise may also adversely affect object detection performance. To mitigate the aforementioned adverse effect, we propose a novel object detection model that employs a Gaussian-Sobel filter as a preprocessor. This filter effectively combines Gaussian blurring to suppress noise effects and the Sobel mask to accentuate object features, enabling effective object detection from LiDAR-derived depth maps. The pro posed model employs RGB images and depth maps, preprocessed with the Gaussian-Sobel filter, to obtain respective detection results using the real-time object detection model, You Only Look Once (YOLO). The YOLO object detection model has been widely used to date. Moreover, new versions of the model are being continuously developed, which exhibit overall better performance in terms of speed and accuracy compared with other object detection models, including single-shot multibox detector (SSD) and Faster R-CNN. Furthermore, YOLO is a lightweight model that can be embedded into on-board systems with limited resources while maintaining a high detection performance. In particular, YOLOv4 is best suited for real-time object detection in real-world environments, as it enables real-time object detection through on-board systems and can detect objects, including small objects, in complex environments [15; 16]. Optimal object locations are estimated by eliminating redundant bounding boxes through non-maximum suppression (NMS). This enables robust object detection, even under challenging conditions such as low-light environments or high-speckle-noise environments, by preserving a high detection accuracy despite the lack of distinct object features or contours. Our test evaluation results demonstrated that the proposed model outperformed existing state-of-the-art models by achieving a 1-7% improvement in average precision (AP), depending on the experimental conditions. The contributions of this study are as follows:  
* We improved image resolution and enhanced object-background segmentation by preprocessing LiDAR-derived depth maps using a fusion of Gaussian blurring and the Sobel mask. * We proposed a versatile object detection model that effectively combines RGB images from cameras and depth maps preprocessed by the Gaussian-Sobel filter.