FiftyFive Tech
  • Clients:

  • Category:

    Artificial Intelligence

  • Services:

    Computer vision algorithms

  • Web:

The port terminal implemented a sophisticated Port Safety Management System, utilizing Python, Tensorflow, Keras, Pandas, computer vision algorithms, and real-time data analysis with Redis, training the AI system to recognize PPE usage, enforce access control, optimize traffic management, and identify unsafe behaviors from CCTV feeds.

The story

A large port terminal faced significant challenges in maintaining safety and security for its operations. Ensuring the well-being of the workforce and the efficient management of the port area was of utmost importance. They needed a comprehensive system to detect and prevent potential safety hazards, monitor traffic management, and enforce safety compliance.

The challenge

The challenges faced by the port terminal were multifaceted. They required a solution that could effectively detect the usage of various personal protective equipment (PPE), including helmets, gloves, aprons, sleeves, respiratory gear, reflective vests, and safety glasses, by deploying diverse cameras across the facility. Additionally, the system needed to monitor and enforce access controls for non-pedestrian areas and no-entry zones, encompassing worker limits, line-of-fire risks, safety under suspended loads, exclusion zones, and time-limited areas.


A robust traffic management system was crucial to regulate the movement of vehicles and pedestrians within the port, enforcing speed limits, restricting vehicle access in specific zones, and ensuring compliance with PPE requirements for vehicle and equipment operators, particularly in mobile plant operations.


Furthermore, the solution had to be capable of analyzing and identifying unsafe behaviors, such as climbing, improper guardrail use, disregard for pedestrian walkways, and working at heights without appropriate PPE, while recognizing various scenarios that posed safety risks.

The solution

In response to these challenges, the port terminal implemented an advanced Port Safety Management System built upon the foundation of Artificial Intelligence and CCTV surveillance. The key components of this innovative solution comprised the utilization of programming languages like Python, machine learning libraries such as Tensorflow and Keras, and data processing tools like Pandas. In addition, the system integrated cutting-edge computer vision algorithms to comprehensively analyze real-time video feeds from strategically positioned cameras throughout the port terminal. To ensure swift and efficient information management and retrieval, real-time data processing was facilitated through Redis.


The AI system underwent rigorous training to proficiently recognize PPE usage, oversee access control, monitor traffic management, and promptly identify unsafe behaviors. Leveraging data from the CCTV cameras, the system applied machine learning algorithms to make informed, real-time decisions, ultimately revolutionizing safety and security protocols within the port terminal.

The outcome

The implementation of the Port Safety Management System yielded substantial benefits. Safety was markedly improved as the system accurately detected PPE usage and enforced safety regulations, reducing accident risks. Access control to non-pedestrian and restricted areas was significantly enhanced, curbing unauthorized entry and minimizing safety hazards. The system’s adept traffic management capabilities optimized the movement of vehicles and pedestrians, ensuring safe operations and lowering the risk of accidents. Most notably, the AI system excelled in identifying and reporting unsafe behaviors, enabling timely interventions and corrections to prevent potential accidents, collectively fostering a safer and more efficient working environment within the port terminal.


In summary, the Port Safety Management System, powered by AI and CCTV technology, transformed the safety and security landscape at the port terminal. It offered a comprehensive solution to address the challenges related to safety, access control, traffic management, and unsafe behavior detection, ultimately resulting in a safer and more efficient working environment.


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