Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects. This paper proposes a new SOD dataset consisting of 39,070 images including 137,121 bird instances, which is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The detail of the challenge with the SOD4SB dataset 1 is introduced in this paper. In total, 223 participants joined this challenge. This paper briefly introduces the award-winning methods. The dataset 2 , the baseline code 3 , and the website for evaluation on the public testset 4 are publicly available.

MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results

Clustering is an important data analytics technique and has numerous use cases. It leads to the determination of insights and knowledge which would not be readily discernible on routine examination of the data. Enhancement of clustering techniques is an active field of research, with various optimisation models being proposed. Such enhancements are also undertaken to address particular issues being faced in specific applications. This paper looks at a particular use case in the maritime domain and how an enhancement of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering results in the apt use of data analytics to solve a real-life issue. Passage of vessels over water is one of the significant utilisations of maritime regions. Trajectory analysis of these vessels helps provide valuable information, thus, maritime movement data and the knowledge extracted from manipulation of this data play an essential role in various applications, viz., assessing traffic densities, identifying traffic routes, reducing collision risks, etc. Optimised trajectory information would help enable safe and energy-efficient green operations at sea and assist autonomous operations of maritime systems and vehicles. Many studies focus on determining trajectory densities but miss out on individual trajectory granularities. Determining trajectories by using unique identities of the vessels may also lead to errors. Using an unsupervised DBSCAN method of identifying trajectories could help overcome these limitations. Further, to enhance outcomes and insights, the inclusion of temporal information along with additional parameters of Automatic Identification System (AIS) data in DBSCAN is proposed. Towards this, a new design and implementation for data analytics called the Multivariate Hierarchical DBSCAN method for better clustering of Maritime movement data, such as AIS, has been developed, which helps determine granular information and individual trajectories in an unsupervised manner. It is seen from the evaluation metrics that the performance of this method is better than other data clustering techniques. 

Multivariate hierarchical DBSCAN model for enhanced maritime data analytics

Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention technologies becomes urgent. The paper presents a novel deep learning model which is developed to detect and alleviate bird strike issues in airport conditions boosting aircraft safety. Based on an extensive database of bird images having different species and flight patterns, the research adopts sophisticated image augmentation techniques which generate multiple scenarios of aircraft operation ensuring that the model is robust under different conditions. The methodology evolved around the building of a spatiotemporal convolutional neural network which employs spatial attention structures together with dynamic temporal processing to precisely recognize flying birds. One of the most important features of this research is the architecture of its dual-focus model which consists of two components, the attention-based temporal analysis network and the convolutional neural network with spatial awareness. The model’s architecture can identify specific features nested in a crowded and shifting backdrop, thereby lowering false positives and improving detection accuracy. The mechanisms of attention of this model itself enhance the model’s focus by identifying vital features of bird flight patterns that are crucial. The results are that the proposed model achieves better performance in terms of accuracy and real time responses than the existing bird detection systems. The ablation study demonstrates the indispensable roles of each component, confirming their synergistic effect on improving detection performance. The research substantiates the model’s applicability as a part of airport bird strike surveillance system, providing an alternative to the prevention strategy. This work benefits from the unique deep learning feature application, which leads to a large-scale and reliable tool for dealing with the bird strike problem. 

A Deep Learning Framework for Real-Time Bird Detection and Its Implications for Reducing Bird Strike Incidents

Birds pose a serious threat to the safe operation of aircrafts. Existing object detection methods have achieved good results for big and medium instances; however, for small flying bird instances, drawbacks such as slow speed, low accuracy, and large model size are still present. Therefore, to overcome these shortcomings, we propose the SMB-YOLOv5 model to detect birds near airports. First, we introduce a self-supervised predictive convolution attention block to enable YOLOv5s6 to focus on critical information, thereby enhancing detection performance. Second, we introduce a multi-branch block (MBB) that enhances the expressive capability of the network by incorporating branches with different receptive fields. Third, to enhance the feature fusion capability of the model and detection mAP@50 for small-bird instances, drawing inspiration from the bidirectional feature pyramid network, we reutilize the shallow-level features of the feature extraction network. We also remove some modules to ensure an increased accuracy without excessively inflating the model size. Finally, to increase the convergence speed of the network, we modify its loss function by replacing complete IoU (CIoU) with efficient IoU (EIoU), which improves the detection mAP@50 of the network. Compared to the YOLOv5s6 model, the proposed SMB-YOLOv5 model achieves a 2.6% increase in mAP@50 on the test dataset. The detection speed has reached 24 fps. We find that the SMB-YOLOv5 has a higher mAP@50 than the other algorithms in the test dataset and the lowest number of parameters, and it can be applied in airport bird detection systems to provide more precise bird orientation information for airport bird detection tasks.

SMB-YOLOv5: A Lightweight Airport Flying Bird Detection Algorithm Based on Deep Neural Networks

As we continue moving into the 4th Industrial Revolution, the significance of Artificial Intelligence (AI), the Internet of Things (IoT), and big data is increasingly pronounced. Among the various applications of these modern technologies, navigation technology stands out as the most impactful. This isn't just limited to helping people find their way; it has diverse uses that range from personal navigation aids and guidance systems in museums to security applications like intrusion detection. Beyond human navigation, these technologies play a vital role in asset tracking, efficient fleet and inventory management, and optimizing manufacturing and distribution processes. An exponential increase in available positioning data drives this boom in location-aware applications. The ubiquity of connectivity and the pervasive nature of the IoT are central to this surge. With devices and sensors everywhere, collecting and transmitting data in real-time, we are seeing ground-breaking progress in solving indoor positioning challenges. As a result, places that were once difficult to navigate or monitor can now be mapped and understood with precision, opening new avenues for innovation and efficiency. This special issue highlights the convergence of Explainable AI (XAI) and the Internet of Things (IoT) in indoor navigation. By emphasizing the innovative use of WIFI signals for positioning and navigation, the issue seeks to illuminate how XAI can make more transparent and user-friendly IoT-driven navigation decisions. Additionally, it intends to explore the practical challenges and real-world applications of integrating these technologies, promoting interdisciplinary research and collaboration across fields like AI, IoT, and wireless communications. 

Preface of Special Issue on Explainable AI-empowered Internet of Things for Indoor Navigation using WIFI Sensing

One fundamental limitation to the research of bird strike prevention is the lack of a large-scale dataset taken directly from real-world airports. Existing relevant datasets are either small in size or not dedicated for this purpose. To advance the research and practical solutions for bird strike prevention, in this paper, we present a large-scale challenging dataset AirBirds that consists of 118,312 time-series images, where a total of 409,967 bounding boxes of flying birds are manually, carefully annotated. The average size of all annotated instances is smaller than 10 pixels in 1920 $$\times $$ 1080 images. Images in the dataset are captured over 4 seasons of a whole year by a network of cameras deployed at a real-world airport, covering diverse bird species, lighting conditions and 13 meteorological scenarios. To the best of our knowledge, it is the first large-scale image dataset that directly collects flying birds in real-world airports for bird strike prevention. This dataset is publicly available at https://airbirdsdata.github.io/ . 

pdf/airbirds-a-large-scale-challenging-dataset-for-bird-strike-1r09kw25.pdf

AirBirds: A Large-scale Challenging Dataset for Bird Strike Prevention in Real-world Airports

TrackPuzzle: Efficient registration of unlabeled PDR trajectories for learning indoor route graph

Over the past years, augmented reality (AR) based on mobile phones has gained great attention. When multiple phones are used in AR applications, collaborative simultaneous localization and mapping (SLAM) is considered one of the enabling technologies, i.e., multiple mobile phones complete the localization and mapping through collaboration. However, the state-of-the-art collaborative SLAM systems not only suffer from the delays introduced by a high-complexity graph optimization problem, but also may exhibit varying levels of accuracy across dissimilar environments or different types of mobile devices. In this paper, we propose a scalable and robust collaborative SLAM system, point-line-based Collaborative SLAM (ColSLAM). Technically, ColSLAM includes two innovative features that help achieve satisfactory scalability and robustness. First, a mapping cacher (MC) is designed for each agent on the server, which uses global keyframes to detect loop closures, updates the cached local map, and quickly responds to the agent's pose drifts. With MC, each agent's local pose is corrected using global knowledge in real-time. Secondly, to improve the robustness performance, ColSLAM employs point-line-fusion-based Visual Inertial Odometry (VIO), point-line-fusion-based NetVLAD loop detection, and an enhanced geometric verification and relative pose calculation method called PNPL. Empirical evaluations based on the EuRoc dataset and real degenerate environments demonstrate that ColSLAM outperforms the existing collaborative SLAM systems in terms of accuracy, robustness, and scalability.

ColSLAM: A Versatile Collaborative SLAM System for Mobile Phones Using Point-Line Features and Map Caching

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AirBirds: A Large-scale Challenging Dataset for Bird Strike Prevention in Real-world Airports

TrackPuzzle: Efficient registration of unlabeled PDR trajectories for learning indoor route graph

ColSLAM: A Versatile Collaborative SLAM System for Mobile Phones Using Point-Line Features and Map Caching