Performance comparison of ICP algorithms. by Mengyuan Yan (14200322)

“…This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. …”

ResNet50 algorithm. by Zhang Shanshan (22272359)

Performance benchmark of detection models. by Mengyuan Yan (14200322)

“…This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. …”

Algorithm comparison test results. by Hang Ren (1312905)

An illustration of the Deepwalk algorithm operation involves a random walk on the graph, resulting in a “walk” sequence, which is then utilized to construct a graph. by Xiaobing Deng (1622233)

Comparison results of multi-classification. by Tao Yang (184280)

Annotation files generated from the training set. by Mengyuan Yan (14200322)

“…This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. …”

Dynamic mapping results of test data. by Mengyuan Yan (14200322)

“…This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. …”

Model evaluation at 100 epochs. by Mengyuan Yan (14200322)

“…This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. …”

Architecture diagram of the aerial module. by Mengyuan Yan (14200322)

“…This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. …”

Point cloud imaging result with UAV data absence. by Mengyuan Yan (14200322)

“…This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. …”

Partial enlarged view of point cloud. by Mengyuan Yan (14200322)

“…This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. …”

Colorized point cloud data. by Mengyuan Yan (14200322)

“…This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. …”

Architecture diagram of the ground module. by Mengyuan Yan (14200322)

“…This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. …”

Visual comparison of detection results. by Heng Zhao (333503)

“…<div><p>Side-scan sonar imaging is essential for underwater target detection in marine exploration and engineering applications, yet small target detection faces significant challenges including limited frequency domain feature utilization, insufficient multi-scale feature fusion, and high computational complexity. …”

Comparison with different detection methods. by Guoqing Zhang (151441)

Comparison diagram of detection accuracy. by Xinwei Wang (352488)

“…This study focuses on the limitations of the YOLOv8s target detection algorithm, including its low efficiency in multi-scale feature processing and insufficient small target detection capability, which hinder its ability to perform rapid and accurate large-scale searches for drones. …”

LC-FPN structure. by Daoze Tang (20454615)

“…<div><p>In the field of UAV aerial image processing, ensuring accurate detection of tiny targets is essential. Current UAV aerial image target detection algorithms face challenges such as low computational demands, high accuracy, and fast detection speeds. …”

Labeling information of the VisDrone dataset. by Daoze Tang (20454615)

“…<div><p>In the field of UAV aerial image processing, ensuring accurate detection of tiny targets is essential. Current UAV aerial image target detection algorithms face challenges such as low computational demands, high accuracy, and fast detection speeds. …”

Differences between models of different scales. by Daoze Tang (20454615)

“…<div><p>In the field of UAV aerial image processing, ensuring accurate detection of tiny targets is essential. Current UAV aerial image target detection algorithms face challenges such as low computational demands, high accuracy, and fast detection speeds. …”