The early displacement monitoring of excavation pits is currently a primary method to prevent pit collapse accidents. However, conventional pit displacement monitoring mainly relies on regular manual measurement or automated professional instrument measurement, which results in high cost, complex operation and deployment, non-real-time measurement, and non-intuitive data collection. Consequently, this paper proposes a method for the visual measurement of three-dimension (3D) displacement of pits based on a two-stage Perspective-n-Point (PNP) algorithm. Initially, markers are installed on the reference points with known 3D spatial coordinates and on each displacement measurement point of the excavation pit. All markers are scanned sequentially by panning the camera and zooming the lens, ensuring at least two adjacent markers enter the frame and are captured in each shot. Secondly, the improved Histogram of Oriented Gradients (HOG) combined with Support Vector Machine (SVM) rapid algorithm is utilized to perform quick detection and coarse positioning of each marker in the aforementioned photos. Thirdly, the sub-pixel algorithm and image morphology are combined. Based on the coarse positioning, the pixel coordinate values of the markers in the images are calculated. Lastly, the two-stage PNP algorithm is applied in a cyclical fashion: by using the known 3D spatial coordinates and its pixel coordinates of the previous marker to solve for the current camera’s 3D spatial coordinates and pose, the 3D spatial coordinates of the next marker can be derived using the current camera’s 3D spatial coordinates and pose and the pixel coordinates of the next marker. This cycle continues until the 3D spatial coordinates of all markers are fully resolved, thereby achieving accurate measurement of the 3D displacement of the pit. Experimental results show that the accuracy of the proposed method is nearly as high as the measurement accuracy of manual total stations, meeting the requirements for daily 3D displacement measurement of excavation pits. Meanwhile, this method offers the advantages of low cost, simple deployment and operation, and a high degree of automation.

Rapid identification and localization of dry bulk cargo hoppers are currently core issues in the automation control of gantry cranes at dry bulk terminals. The current conventional method relies on LiDAR systems for the identification and positioning of bulk unloading hoppers. However, this approach is complex and costly. In contrast, GPS-based positioning solutions for bulk unloading hoppers are prone to damage due to the vibrations generated during the operation process. Therefore, in this paper, a hopper localization system based on monocular camera vision is proposed to locate the position of the bulk unloading hopper. The hopper identification and localization process are divided into three stages. The first stage uses the improved YOLOv5 model to quickly and roughly locate the hopper target. The second stage uses morphological geometrical features to locate the corner points of the hopper target. The third stage determines the three-dimensional coordinates of the hopper target by solving the position of the corner points in the world coordinate system through the PnP (Perspective-n-Point) algorithm. The experimental results show that the average positioning accuracy of the coordinates of the method is above 93%, demonstrating the accuracy and effectiveness of the method.

In container terminal operations, truck lifting accidents pose a serious threat to the safety and efficiency of automated equipment. Traditional detection methods using visual cameras and single-line Light Detection and Ranging (LiDAR) are insufficient for capturing three-dimensional spatial features, leading to reduced detection accuracy. Moreover, the boundary features of key accident objects, such as containers, truck chassis, and wheels, are often blurred, resulting in frequent false and missed detections. To tackle these challenges, this paper proposes an accident detection method based on multi-line LiDAR and an improved PointNet++ model. This method uses multi-line LiDAR to collect point cloud data from operational lanes in real time and enhances the PointNet++ model by integrating a multi-layer perceptron (MLP) and a mixed attention mechanism (MAM), optimizing the model’s ability to extract local and global features. This results in high-precision semantic segmentation and accident detection of critical structural point clouds, such as containers, truck chassis, and wheels. Experiments confirm that the proposed method achieves superior performance compared to the current mainstream algorithms regarding point cloud segmentation accuracy and stability. In engineering tests across various real-world conditions, the model exhibits strong generalization capability.

To address the high-precision measurement requirements for container pose on dual-trolley quayside crane-transfer platforms, this paper proposes a machine vision-based measurement method that resolves the challenges of multi-scale lockhole detection and precision demands caused by complex illumination and perspective deformation in port operational environments. A hardware system comprising fixed cameras and edge computing modules is established, integrated with an adaptive image-enhancement preprocessing algorithm to enhance feature robustness under complex illumination conditions. A multi-scale adaptive frequency object-detection framework is developed based on YOLO11, achieving improved detection accuracy for multi-scale lockhole keypoints in perspective-distortion scenarios (mAP@0.5 reaches 95.1%, 4.7% higher than baseline models) through dynamic balancing of high–low-frequency features and adaptive convolution kernel adjustments. An enhanced EPnP optimization algorithm incorporating lockhole coplanar constraints is proposed, establishing a 2D–3D coordinate transformation model that reduces pose-estimation errors to millimeter level (planar MAE-P = 0.024 m) and sub-angular level (MAE-$\theta$ = 0.11°). Experimental results demonstrate that the proposed method outperforms existing solutions in container pose-deviation-detection accuracy, efficiency, and stability, proving to be a feasible measurement approach.