## 2 State-of-the-Art Review
Several methods have been proposed for error detection in STS container crane operations. The authors of [32] introduced a new method for detecting crane rails' wear, significantly improving accuracy, while [33] identified and assessed human errors among tower crane operators, focusing on the importance of monitoring the anti-collision system. The authors of [34] addressed the issue of detecting physical impacts to the corners of shipping containers during handling operations using the impacts detection methodology. It used the impacts detection methodology to detect recurrent quay crane impacts on the same container locations during hooking. These contemporaneous hits suggest false  
Figure 2: Error as a precursor to failures [25]. hooking, which could cause severe metal deformations over short durations. The study highlights the necessity of shipping container damage detection for port safety and efficiency. In [35], the authors developed a high-speed colour-based object detection algorithm for quayside crane operator assistance systems, significantly increasing the detection rate while maintaining high accuracy. These studies collectively highlight the importance of continuous improvement in error detection methods to enhance safety and efficiency in STS container crane operations. Several studies have demonstrated the efficacy of deep learning for error prediction in container crane operations. Timely error detection is essential in optimising crane performance and guaranteeing safety. Unnoticed errors or problems can compromise production and safety by causing inefficiencies, equipment damage, and possible accidents [36]. Human error is a significant contributing factor in accidents during mobile crane operations. Effective crane control and precise positioning depend on pre-established speeds for each direction, determined according to the crane system's specifications. Detecting problems in speed, acceleration, or deceleration promptly can enhance performance and avoid overloading or crashes. Developing intelligent assistance systems that detect real-time errors can enhance crane operation safety by reducing the likelihood of human errors [37]. Proficient expertise is necessary for the safe operation of overhead cranes and to minimise the occurrence of equipment malfunctions. Nevertheless, the lack of skilled operators has been a worry recently. Early identification of errors, such as problems with rope tension or differences in load weight, can aid in the prevention of accidents and equipment damage, thereby enhancing safety without only depending on the operator's skills [38]. Ensuring prompt identification of errors is essential in successfully implementing efficient automated crane trajectories and providing a user-friendly interface that assists operators in choosing and executing movements. Timely identification of errors in trajectory planning or execution can enhance performance and prevent accidents [36]. The manual planning process currently employed for crane lift path planning is arduous and error-prone, primarily due to construction sites' dynamic and congested conditions. Implementing automated systems capable of real-time error or collision detection can enhance operational efficiency and precision [39]. The research on error prediction in STS container crane operations is diverse, covering various aspects of crane operations. The authors of [40] focused on the fatigue damage analysis of ageing steel structures, a critical factor in error prediction. The authors of [41] identified specific risk factors in stevedoring operations, which can inform error prediction strategies. In [42], the authors addressed the need for robust control systems in offshore container cranes, which can help mitigate errors. The authors of [43] highlighted the importance of structural health monitoring in error prediction, providing a potential method for early detection. Finally, [44] identified a decrease in the mean time between failures due to heavy operations, increased maintenance incidents, and man-hours.