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.

In the process of automatic container terminal loading and unloading, the three-dimensional attitude of the container affects the security of loading and unloading operations, so the three-dimensional attitude positioning of the container is very important. In this paper, a visual non-contact measurement method is used to realize the real-time orientation of the three-dimensional attitude of the container. First, the container corner is coarsely positioned by a small-scale deep learning network. Secondly, the precise position of the container keyhole is obtained by the secondary positioning of the container corner through the traditional image processing algorithm, and the container posture is measured in three dimensions by combining the physical motion model of the container during loading and unloading. After testing, unlike previous measurement methods, the measurement accuracy of this method met the requirements of automatic loading and unloading of container terminals, and the measurement time met the requirements of real-time measurement.

Truck-lifting accidents are common in container-lifting operations. Previously, the operation sites are needed to arrange workers for observation and guidance. However, with the development of automated equipment in container terminals, an automated accident detection method is required to replace manual workers. Considering the development of vision detection and tracking algorithms, this study designed a vision-based truck-lifting prevention system. This system uses a camera to detect and track the movement of the truck wheel hub during the operation to determine whether the truck chassis is being lifted. The hardware device of this system is easy to install and has good versatility for most container-lifting equipment. The accident detection algorithm combines convolutional neural network detection, traditional image processing, and a multitarget tracking algorithm to calculate the displacement and posture information of the truck during the operation. The experiments show that the measurement accuracy of this system reaches 52 mm, and it can effectively distinguish the trajectories of different wheel hubs, meeting the requirements for detecting lifting accidents.

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.