Underwater Biological Detection Algorithm Based on Improved Faster-RCNN

GEB-YOLO: a novel algorithm for enhanced and efficient detection of foreign objects in power transmission lines

Detecting foreign objects in power transmission lines is essential for mitigating safety risks and maintaining line stability. Practical detection, however, presents challenges including varied target sizes, intricate backgrounds, and large model weights. To address these issues, this study introduces an innovative GEB-YOLO model, which balances detection performance and quantification. Firstly, the algorithm features a lightweight architecture, achieved by merging the GhostConv network with the advanced YOLOv8 model. This integration considerably lowers computational demands and parameters through streamlined linear operations. Secondly, this paper proposes a novel EC2f mechanism, a groundbreaking feature that bolsters the model's information extraction capabilities. It enhances the relationship between weights and channels via one-dimensional convolution. Lastly, the BiFPN mechanism is employed to improve the model's processing efficiency for targets of different sizes, utilizing bidirectional connections and swift feature fusion for normalization. Experimental results indicate the model's superiority over existing models in precision and mAP, showing improvements of 3.7 and 6.8%, respectively. Crucially, the model's parameters and FLOPs have been reduced by 10.0 and 7.4%, leading to a model that is both lighter and more efficient. These advancements offer invaluable insights for applying laser technology in detecting foreign objects, contributing significantly to both theory and practice.

Research Challenges, Recent Advances, and Popular Datasets in Deep Learning-Based Underwater Marine Object Detection: A Review

Underwater marine object detection, as one of the most fundamental techniques in the community of marine science and engineering, has been shown to exhibit tremendous potential for exploring the oceans in recent years. It has been widely applied in practical applications, such as monitoring of underwater ecosystems, exploration of natural resources, management of commercial fisheries, etc. However, due to complexity of the underwater environment, characteristics of marine objects, and limitations imposed by exploration equipment, detection performance in terms of speed, accuracy, and robustness can be dramatically degraded when conventional approaches are used. Deep learning has been found to have significant impact on a variety of applications, including marine engineering. In this context, we offer a review of deep learning-based underwater marine object detection techniques. Underwater object detection can be performed by different sensors, such as acoustic sonar or optical cameras. In this paper, we focus on vision-based object detection due to several significant advantages. To facilitate a thorough understanding of this subject, we organize research challenges of vision-based underwater object detection into four categories: image quality degradation, small object detection, poor generalization, and real-time detection. We review recent advances in underwater marine object detection and highlight advantages and disadvantages of existing solutions for each challenge. In addition, we provide a detailed critical examination of the most extensively used datasets. In addition, we present comparative studies with previous reviews, notably those approaches that leverage artificial intelligence, as well as future trends related to this hot topic.

Marine Robotics 4.0: Present and Future of Real-Time Detection Techniques for Underwater Objects

Underwater marine robots (UMRs), such as autonomous underwater vehicles, are promising alternatives for mankind to perform exploration tasks in the sea. These vehicles have the capability of exploring the underwater environment with onboard instruments and sensors. They are extensively used in civilian applications, scientific studies, and military missions. In recent years, the flourishing growth of deep learning has fueled tremendous theoretical breakthroughs and practical applications of computer-vision-based underwater object detection techniques. With the integration of deep-learning-based underwater object detection capability on board, the perception of underwater marine robots is expected to be enhanced greatly. Underwater object detection will play a key role in Marine Robotics 4.0, i.e., Industry 4.0 for Marine Robots. In this chapter, one of the key research challenges, i.e., real-time detection of underwater objects, which has prevented many real-world applications of object detection techniques onboard UMRs, is reviewed. In this context, state-of-the-art techniques for real-time detection of underwater objects are critically analyzed. Futuristic trends in real-time detection techniques of underwater objects are also discussed.