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Object detection on drone-view images is vital for applications like intelligent transportation, abnormal behavior detection, and urban surveillance. However, the diverse perspectives and altitudes from which Unmanned Aerial Vehicle (UAV) capture scenes pose challenges due to multi-scale differences and small object sizes. To address this, we propose CSSDet, emphasizing multi-scale object detection and small object enhancement. CSSDet integrates an additional detection head for small objects and employs a bidirectional weighted feature pyramid network for effective cross-scale feature fusion, enhancing global perceptual capability. Additionally, we introduce coordinate attention and Involution modules to mitigate information loss. Experiments are conducted on two publicly available datasets, VisDrone and UAVDT. Quantitative results from the experiments indicate that the proposed CSSDet achieves optimal performance across a wide range of evaluation metrics, with particular strengths demonstrated in multi-scale object detection and small-size object detection. Qualitative results showcase its remarkable effectiveness in various aspects of object detection, including small object detection, objects at different scales and categories, multi-perspective scenarios, and complex scenes. The substantial experimental findings confirm that CSSDet attains comprehensive object detection performance on drone-view images. The source code is available at https://github.com/Gui-Cheng/CSSDet.

Integrating multiple hyperspectral linear-array cameras into a Linear-array Multi-camera System (LMS) enables an expanded Field of View (FOV) while preserving high spatial resolution. However, the constrained capabilities of tie points are weakened by the limited overlap and short baseline between sub-cameras, hindering the attainment of reliable solutions for the theoretically ideal relative calibration model. Additionally, the absence of stereoscopic imaging in the LMS introduces the challenge of acquiring dense Ground Control Points (GCPs) throughout the FOV during calibration. In this paper, for calibrating an advanced airborne hyperspectral LMS named Arial Hyperspectral System-1000 (AHS-1000), we propose an equivalent geometric model that transforms the three-camera configuration of the LMS into a single-camera representation on the master camera’s focal plane, which was successfully applied to the data processing system of AHS-1000. We extracted dense GCPs by matching each Level-1 sub-image of the LMS with the bundled images of an existing three-line-scanner aerial image dataset, whose orientation parameters have been accurately solved by bundle adjustment with sparse GCPs; the matched points of each sub-image were consistently projected onto the master focal plane as image observations for our equivalent model. Finally, we established a collinearity equation system and performed a combined strip adjustment to calibrate the distortion terms for each pixel of the equivalent camera. The results demonstrate the effectiveness and robustness of our equivalent modeling method, which outperforms the classic modeling method by 2.83 pixels in interior accuracy and 0.86 pixels in relative accuracy, as well as outperforming another similar equivalent modeling method by 1.22 pixels in interior accuracy and 0.18 pixels in relative accuracy. Additionally, our comparison utilizing bundled images, public DOM (Digital Orthophoto Maps) /DEM (Digital Elevation Models), and high-precision DOM/DEM from aerial three-line-scanner demonstrates that bundled images can achieve higher calibration accuracy.

Fatigue is an important cause of traffic crashes, and effective fatigue detection models can reduce these crashes. Research has found large differences in fatigued driving performance from driver to driver, as well as a significant cumulative effect of fatigue on a given driver over time. Both sources of variation can decrease the accuracy of detection systems, but previous studies have not done enough to evaluate these differences. The purpose of this study is therefore to develop a fatigue detection model that considers individual differences and the time cumulative effect of fatigue. Data on the lateral position of the car in its lane, steering wheel movement, speed, and eye movement were collected from 22 drivers using a driving simulator with an eye-tracking system. Drivers’ subjective fatigue scores were collected using the Karolinska Sleepiness Scale. State space models (SSMs) were built to detect fatigue in each driver, considering his or her individual features. As a time series model, the SSM can also address the time cumulative effect of fatigue, and it does not require a large dataset to achieve high levels of accuracy. The differences in SSM results confirm that diversity does exist among drivers’ fatigued driving performance, so the ability of the SSM to take into account driver-specific information from each individual driver suggests that it is more suitable for fatigue detection than models that use aggregated driver data. Results show that the fatigue detection accuracy of the SSM (77.73%) is higher than that of artificial neural network models (61.37%). The advantages of accuracy, high interpretability, and flexibility make the SSM a comprehensive and valuable individualized fatigue detection model for commercial use.

Traditionally, traffic characteristics such as speed, volume, and travel time are obtained from a range of sensors and systems such as inductive loop detectors (ILDs), automatic number plate recognition cameras (ANPR), and GPS-equipped floating cars. However, many issues associated with these data have been identified in the existing literature. Although roadside surveillance cameras cover most road segments, especially on freeways, existing techniques to extract traffic data (e.g., speed measurements of individual vehicles) from video are not accurate enough to be employed in a proactive traffic management system. Therefore, this paper aims to develop a technique for estimating traffic data from video captured by surveillance cameras. This paper then develops a deep learning-based video processing algorithm for detecting, tracking, and predicting highly disaggregated vehicle-based data, such as trajectories and speed, and transforms such data into aggregated traffic characteristics such as speed variance, average speed, and flow. By taking traffic observations from a high-quality LiDAR sensor as ‘ground truth’, the results indicate that the developed technique estimates lane-based traffic volume with an accuracy of 97%. With the application of the deep learning model, the computer vision technique can estimate individual vehicle-based speed calculations with an accuracy of 90–95% for different angles when the objects are within 50 m of the camera. The developed algorithm was then utilised to obtain dynamic traffic characteristics from a freeway in southern China and employed in a statistical model to predict monthly crashes.