Using computer vision for underwater detection

The world’s oceans, lakes, and rivers remain largely unexplored, with over 80% of the ocean still unobserved. Additionally, it is estimated that over 14 million tons of plastic enter the ocean annually, significantly impacting marine ecosystems. 

Underwater detection can play an important role in marine operations, from scientific research to infrastructure maintenance. However, traditional underwater monitoring methods rely on divers, sonar, and remote-operated vehicles (ROVs), which can be costly, time-consuming, and limited by environmental conditions.

With advancements in computer vision for underwater detection, AI-driven models like Ultralytics YOLO11 can offer an innovative approach. By leveraging tasks like real-time object detection and tracking, YOLO11 can bring speed, accuracy, and scalability to underwater applications. Whether it’s monitoring marine life, inspecting submerged structures, or identifying debris on the ocean floor, YOLO11 can help streamline automated underwater operations.

In this article, we’ll explore the challenges of traditional underwater detection and how computer vision models like YOLO11 can support more efficient workflows in marine environments.

Challenges in underwater detection

Despite technological advancements, underwater exploration and monitoring still face several challenges:

• Limited visibility: Murky waters, low light, and suspended particles reduce visibility, making it difficult to detect and identify objects accurately.
• Harsh environmental conditions: Strong currents, high pressure, and unpredictable water conditions make manual inspections and traditional monitoring methods challenging.
• High operational costs: Conducting underwater surveys and inspections requires expensive equipment, trained divers, and extensive logistical support.
• Slow data processing: Traditional sonar and camera-based methods often require post-processing, leading to delays in decision-making.

These challenges highlight the need for innovative solutions. Automated and scalable AI solutions can help enhance underwater monitoring, streamline operations, and improve data accuracy.How Vision AI can enhance marine monitoringComputer vision models like YOLO11 can bring precision, efficiency, and adaptability to marine monitoring applications. Its ability to detect and classify objects in real-time makes it a valuable tool for tracking marine life, detecting underwater waste, and ensuring human safety in aquatic environments.Here’s how YOLO11’s features can be leveraged in marine monitoring:

• Real-time detection: YOLO11 can process underwater images and videos at high speed, enabling instant identification of waste, marine species, and human activity beneath the surface.
  
  
• High precision: The model can be trained to detect and classify fish species, count marine life populations, and identify waste deposits with accuracy, even in complex underwater environments.
  
  
• Custom adaptability: YOLO11 can be trained on specific marine datasets, allowing it to detect various species of fish, monitor changes in aquatic ecosystems, and assist in conservation efforts.
  
  
• Edge AI compatibility: The model can be deployed on underwater drones or remote monitoring systems, making it a flexible resource for large-scale marine surveillance while optimizing power and computing resources.

By integrating YOLO11 into marine monitoring workflows, researchers, environmental agencies, and aquaculture industries can improve conservation efforts, optimize marine resource management, and enhance safety for divers and swimmers.

‍Practical applications of YOLO11 in underwater environments

‍Now that we’ve discussed the challenges of underwater detection and how computer vision models like YOLO11 can enhance marine monitoring, let’s explore some of its real-world applications where it can enhance efficiency and accuracy. 

By leveraging object detection, tracking, and classification, YOLO11 supports marine research, underwater inspections, and environmental monitoring.

‍Marine life monitoring

‍Monitoring marine biodiversity is essential for conservation, aquaculture, and ecosystem health assessments. YOLO11 can assist in marine life studies by detecting fish species in real time. By analyzing underwater footage, researchers can identify different fish present in an area, allowing them to assess population trends and migration patterns.

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For instance, YOLO11 can also count fish populations with high accuracy. This capability is particularly useful in fisheries and marine research, where estimating fish numbers is critical for sustainable management. By automating this process, YOLO11 provides valuable insights into overfishing risks and helps develop better conservation strategies.

In commercial aquaculture, fish counting can help track stock levels and optimize farming operations. By continuously monitoring fish populations, operators can make informed decisions about harvesting and restocking, improving efficiency in fish farming practices.

Underwater waste detection

Pollution and waste accumulation in oceans, lakes, and rivers pose severe environmental threats, damaging marine ecosystems and contributing to water contamination. Computer vision models like YOLO11 can provide an efficient method for detecting and categorizing underwater waste, enabling faster cleanup and mitigation efforts.

By mounting underwater cameras or drones integrated with YOLO11, environmental agencies can scan seabeds and water columns to identify plastic waste, fishing nets, and other debris. These AI-powered systems help pinpoint pollution hotspots, ensuring that cleanup efforts are targeted and efficient.

By automating underwater waste detection, YOLO11 supports large-scale cleanup initiatives, promoting healthier aquatic ecosystems.

Submerged infrastructure inspection

Bridges, pipelines, offshore wind farms, and underwater tunnels require regular inspections to ensure structural integrity and safety. Traditional inspection methods rely on divers or remotely operated vehicles (ROVs), which can be costly, time-consuming, and risky in harsh underwater environments.

YOLO11 can enable automated defect detection in submerged structures. For instance, AI-driven cameras mounted on ROVs or underwater drones can identify cracks, corrosion, or other structural anomalies in pipelines and bridge foundations. By using computer vision for underwater detection, maintenance teams can conduct faster and more accurate inspections without needing divers to perform high-risk tasks.

For example, YOLO11 can employed to analyze underwater pipeline footage and detect early signs of damage, helping engineers prevent costly failures. This proactive approach to infrastructure maintenance can result in enhanced safety and extend the lifespan of critical structures.

Detecting divers underwater

Safety is a top priority for underwater exploration, and YOLO11 can play a crucial role in tracking divers during deep-sea operations. By using AI-powered underwater monitoring systems, researchers, rescue teams, and commercial diving companies can detect divers in real-time, ensuring they remain safe.

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YOLO11 can be deployed on underwater cameras to track diver movement and count personnel in active diving zones. Additionally, AI-powered monitoring enhances diver tracking by detecting their presence in specific zones and providing insights into underwater movement patterns. This capability can contribute to improved safety measures by supporting situational awareness and ensuring divers remain within designated operational zones.

By integrating YOLO11 into underwater safety systems, diving teams can enhance their security measures and improve emergency response times in high-risk environments.

Detecting swimmers in pools

AI-powered swimmer detection can help enhance safety in pools, particularly in large aquatic centers or open-water swimming events. Vision AI models like YOLO11 can detect and track swimmers, helping lifeguards monitor activity and identify potential distress situations more efficiently.

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YOLO11 can be trained to count swimmers in real-time, helping to prevent overcrowding and ensuring compliance with safety regulations. For large-scale water sports events, YOLO11-powered drones can provide aerial monitoring, tracking swimmers across open waters. This AI-driven approach to swimmer detection enhances safety measures, reducing response times and improving overall security in aquatic environments.

Benefits of using YOLO11 for underwater detection

Adopting computer vision for underwater detection can introduce a new level of precision and efficiency to marine monitoring. 

By automating tasks such as object detection, classification, and tracking, models like YOLO11 can stand to mean more streamlined workflows and a reduction in reliance on manual inspections. Here are some key benefits:

• Increased efficiency: Automating underwater monitoring and inspections can reduce reliance on manual labor, speeding up operations.
  
  
• Improved accuracy: YOLO11’s real-time object detection streamlines data collection and can help minimize errors in identification.
  
  
• Cost reduction: AI-driven inspections can reduce the need for costly diver operations and overall operational expenses.
  
  
• Scalability: Models like YOLO11 can be deployed across various marine environments, from coastal waters to deep-sea exploration.
  
  
• Environmental impact: Enhancing waste detection and marine monitoring supports conservation efforts and helps protect aquatic ecosystems.

Key takeaways

‍As underwater exploration and monitoring demand more efficient solutions, computer vision models like YOLO11 offer practical advancements. By automating tasks such as marine life tracking, pollution detection, and infrastructure inspection, YOLO11 can enable smarter workflows and support better decision-making in marine environments.

Whether it’s improving ocean conservation, enhancing underwater inspections, or assisting in shipwreck exploration, YOLO11 demonstrates the potential of computer vision in enhancing underwater detection. Explore how YOLO11 can contribute to more effective marine solutions, one innovative application at a time.

Get started with YOLO11 and join our community to learn more about the use cases of computer vision. Discover how YOLO models are driving advancements across industries, from agriculture to self-driving systems. Check out our licensing options to begin your Vision AI projects today.