Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models

Shallow mud detection algorithm for submarine channels based on improved YOLOv5s

Submarine mud poses a risk to channel navigation safety. Traditional detection methods lack efficiency and accuracy. As a result, this paper proposed an enhanced shallow submarine mud detection algorithm, leveraging an improved YOLOv5s model to increase accuracy and effectiveness in identifying such hazards in marine channels. Firstly, the sub-bottom profiler was employed to assess the submarine channel of Lianyungang Port to acquire the image data of the shallow mud sound print. Concurrently, the analysis incorporated the characteristics of changes in sound intensity peaks to precisely identify the shallow mud's location. Furthermore, the incorporation of C2F feature module into the backbone module enhances the gradient flow of the algorithm, augments the feature extraction information, and improves the algorithm's detection performance. Subsequently, Efficient Multi-Scale Attention (EMA) mechanism is incorporated into the neck module, aiming to optimize the algorithm's channel dimensions, minimize computational overhead, and enhance its detection efficiency. Finally, the study introduced Normalized Wasserstein Distance (NWD) loss function into bounding box regression loss function. This integration effectively addresses the issue of multi-scale defects, emphasizes the transformation of target planar position deviation, and improves the accuracy of the algorithm's detection capabilities. The results indicate that the improved YOLOv5s-EF algorithm outperforms the original YOLOv5s algorithm and other widely used detection algorithms. It achieved a validation set precision rate of 97.8%, recall rate of 97.6%, F1 value of 97.7%, mean Average Precision (mAP)@0.5 of 98.2%, mAP@0.95 of 69.6%, and Frames Per Second (FPS) of 51.8. YOLOv5s-EF algorithm proposed in this study offers a novel technical approach for detecting mud in submarine channels, which is importance for ensuring the safe operation and maintenance of dredging in such channels.

An Improved YOLOv5-Based Lightweight Submarine Target Detection Algorithm

Submarine recognition plays a critical role in maritime security and military defense. However, traditional submarine recognition algorithms face limitations in feature representation capability and robustness. Additionally, deploying deep learning methods on embedded and mobile platforms presents a bottleneck. To address these challenges, we propose an innovative and practical approach-an improved YOLOv5-based lightweight submarine automatic recognition detection algorithm. Our method leverages the Feature Pyramid based on MobileNetV3 and the C3_DS module to reduce computation and parameter complexity while ensuring high precision in submarine recognition. The integration of the adaptive neck from the SA-net strategy further mitigates missed detections, significantly enhancing the accuracy of submarine target detection and recognition. We evaluated our improved model on a submarine dataset, and the results demonstrate remarkable advancements in Precision, Recall, and mAP0.5, with respective increases of 8.54%, 6.02%, and 3.36%. Moreover, we achieved a notable reduction of 34.1% in parameter quantity and 67.9% in computational complexity, showcasing its lightweight effects. Overall, our proposed method introduces novel improvements to submarine recognition, addressing existing limitations and offering practical benefits for real-world deployment on embedded and mobile platforms. The enhanced performance in precision and recall metrics, coupled with reduced computational requirements, emphasizes the significance of our approach in enhancing maritime security and military applications.

Evaluating the Performance of Video-Based Automated Passenger Counting Systems in Real-World Conditions: A Comparative Study

Automatic passenger counting (APC) systems in public transport are useful in collecting information that can help improve the efficiency of transport networks. Focusing on video-based passenger counting, the aim of this study was to evaluate and compare an existing APC system, claimed by its manufacturer to be highly accurate (98%), with a newly developed low-cost APC system operating under the same real-world conditions. For this comparison, a low-cost APC system using a Raspberry Pi with a camera and a YOLOv5 object detection algorithm was developed, and an in-field experiment was performed in collaboration with the public transport companies operating in the cities of Turin and Asti in Italy. The experiment shows that the low-cost system was able to achieve an accuracy of 72.27% and 74.59%, respectively, for boarding and alighting, while the tested commercial APC system had an accuracy, respectively, of 53.11% and 55.29%. These findings suggest that current APC systems might not meet expectations under real-world conditions, while low-cost systems could potentially perform at the same level of accuracy or even better than very expensive commercial systems.

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent's attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the "feature store & return" module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models.

Fight Fire with Fire: Detecting Forest Fires with Embedded Machine Learning Models Dealing with Audio and Images on Low Power IoT Devices

Forest fires are the main cause of desertification, and they have a disastrous impact on agricultural and forest ecosystems. Modern fire detection and warning systems rely on several techniques: satellite monitoring, sensor networks, image processing, data fusion, etc. Recently, Artificial Intelligence (AI) algorithms have been applied to fire recognition systems, enhancing their efficiency and reliability. However, these devices usually need constant data transmission along with a proper amount of computing power, entailing high costs and energy consumption. This paper presents the prototype of a Video Surveillance Unit (VSU) for recognising and signalling the presence of forest fires by exploiting two embedded Machine Learning (ML) algorithms running on a low power device. The ML models take audio samples and images as their respective inputs, allowing for timely fire detection. The main result is that while the performances of the two models are comparable when they work independently, their joint usage according to the proposed methodology provides a higher accuracy, precision, recall and F1 score (96.15%, 92.30%, 100.00%, and 96.00%, respectively). Eventually, each event is remotely signalled by making use of the Long Range Wide Area Network (LoRaWAN) protocol to ensure that the personnel in charge are able to operate promptly.