The increasing prevalence of unmanned aerial vehicles (UAVs), commonly known as drones, has generated a demand for reliable detection systems. The inappropriate use of drones presents potential security and privacy hazards, particularly concerning sensitive facilities. Consequently, a critical necessity revolves around the development of a proficient system with the capability to precisely identify UAVs and other flying objects even in challenging scenarios. Although advancements have been made in deep learning models, obstacles such as computational intricacies, precision limitations, and scalability issues persist. To overcome those obstacles, we proposed the concept of MultiFeatureNet (MFNet), a solution that enhances feature representation by capturing the most concentrated feature maps. Additionally, we present MultiFeatureNet-Feature Attention (MFNet-FA), a technique that adaptively weights different channels of the input feature maps. To meet the requirements of multi-scale detection, we presented the versions of MFNet and MFNet-FA, namely the small (S), medium (M), and large (L). The outcomes reveal notable performance enhancements. For optimal bird detection, MFNet-M (Ablation study 2) achieves an impressive precision of 99.8%, while for UAV detection, MFNet-L (Ablation study 2) achieves a precision score of 97.2%. Among the options, MFNet-FA-S (Ablation study 3) emerges as the most resource-efficient alternative, considering its small feature map size, computational demands (GFLOPs), and operational efficiency (in frame per second). This makes it particularly suitable for deployment on hardware with limited capabilities. Additionally, MFNet-FA-S (Ablation study 3) stands out for its swift real-time inference and multiple-object detection due to the incorporation of the FA module. The proposed MFNet-L with the focus module (Ablation study 2) demonstrates the most remarkable classification outcomes, boasting an average precision of 98.4%, average recall of 96.6%, average mean average precision (mAP) of 98.3%, and average intersection over union (IoU) of 72.8%. To encourage reproducible research, the dataset, and code for MFNet are freely available as an open-source project: github.com/ZeeshanKaleem/MultiFeatureNet .

Unmanned aerial vehicles (UAVs) are popular in various applications due to their mobility, size, and user-friendliness. However, identifying malicious UAVs presents challenges that need to be encountered in general image-based object detection. These challenges arise because UAVs can fly at different altitudes, making it challenging to distinguish them from other flying objects and identify their size. In addition, the speed of UAVs also adds to the difficulty of capturing their clear images, which can lead to blurring, particularly in complex backgrounds. To address these challenges, we present an improved YOLOv5 architecture named M ulti- S cale F eature map G hostNet ( MSF-GhostNet ) by introducing GhostConv (GC) and C3Ghost (C3G) modules to reduce the redundant operations in the head and neck. We also proposed three feature map combinations to evaluate the performance of multi-scale and multi-target flying objects, including drones, birds, planes, and helicopters. This approach significantly reduces the waste of computing resources when detecting small-sized flying objects. We also integrated auto-anchor and batch size mechanisms to ensure efficient model training and avoid over-fitting. Our proposed model showed 1.25% fewer false positives (FP) than the state-of-the-art GhostNet-YOLOv5 model. The proposed MSF-GhostNet outperformed GhostNet-YOLOv5 with higher precision, recall, and F1 scores (1.3%, 5.3%, and 3.7% respectively) and reduced model parameters and model size by 3.1% and 4.1%, respectively. The proposed solution also outperformed several other state-of-the-art algorithms exists in the literature. We also provided dataset and code as an open-source: github.com/ZeeshanKaleem/MSF-GhostNet .

In quantum computing, noisy intermediate-scale quantum (NISQ) devices offer unprecedented computational capabilities but are vulnerable to errors, notably measurement inaccuracies that impact computation accuracy. This study explores the efficacy of error mitigation techniques in improving quantum circuit performance on NISQ devices. Techniques such as dynamic decoupling (DD), twirled readout error extraction (T-REx) and zero-noise extrapolation (ZNE) are examined through extensive experimentation on an ideal simulator, IBM Kyoto, and IBM Osaka quantum computers. Results reveal significant performance discrepancies across scenarios, with error mitigation techniques notably enhancing both estimator result and variance values, aligning more closely with ideal simulator outcomes. The comparison results with ideal simulator (having expected result value 0.8284) shows that T-Rex has improved results on IBM Kyoto and enhanced average expected result value from 0.09 to 0.35. Similarly, DD has improved average expected result values from 0.2492 to 0.3788 on IBM Osaka. These findings underscore the critical role of error mitigation in bolstering quantum computation reliability. The results suggest that selection of mitigation technique depends upon quantum circuit and its depth, type of hardware and operations to be performed.

Falls are a major public health concern among the elderly and the number of gadgets designed to detect them has increased significantly in recent years. This document provides a detailed summary of research done on fall detection systems, with comparisons across different types of studies. Its purpose is to be a resource for doctors and engineers who are planning or conducting field research. Following the examination, datasets, limitations, and future imperatives in fall detection were discussed in detail. The quantity of research using context-aware approaches continues to rise, but there is a new trend toward integrating fall detection into smartphones, as well as the use of artificial intelligence in the detection algorithm. Concerns with real-world performance, usability, and reliability are also highlighted.

The global metal detector market is predicted to grow to USD 2.74 billion by 2030, with a compound annual growth rate (CAGR) of 7.66%, indicating potential expansion in the industry. Metallic and non-metallic material/object detection is pivotal to various technical applications and paramount in ensuring structural integrity, operational efficiency, and overall safety. This study analyzes a multi-sensor fusion approach for classification of metallic and non-metallic objects in complex soil environments at various depths. The adopted approach is beneficial in scenarios where single-sensor systems fail to provide adequate performance. We investigated the integration of magnetic induction spectroscopy (MIS) and ground penetrating radar (GPR) sensors for binary, soil, depth, and multiclass classification. A dataset comprising 14 objects was trained and tested on six AI models (900 variants). The best-performing 384 model variants, which have the highest accuracy, precision, and recall, reaching 98%+ and with minimal Type I and II errors, are reported here. The best accuracy for each classification problem (binary, soil type, depth and multi object) corresponding to each set of input data (MIS, GPR, MIS+GPR) has been summarized and reported. The analysis shows that different AI models has performed better in different scenarios. The study’s implications extend to defense, archaeology, and geology, offering enhanced object detection in complex soil environments. These findings underscore the multi-sensor fusion approach’s significance for advancements in remote sensing and classification technologies.

The usage of drones has tremendously increased in different sectors spanning from military to industrial applications. Despite all the benefits they offer, their misuse can lead to mishaps, and tackling them becomes more challenging particularly at night due to their small size and low visibility conditions. To overcome those limitations and improve the detection accuracy at night, we propose an object detector called Ghost Auto Anchor Network (GAANet) for infrared (IR) images. The detector uses a YOLOv5 core to address challenges in object detection for IR images, such as poor accuracy and a high false alarm rate caused by extended altitudes, poor lighting, and low image resolution. To improve performance, we implemented auto anchor calculation, modified the conventional convolution block to ghost-convolution, adjusted the input channel size, and used the AdamW optimizer. To enhance the precision of multiscale tiny object recognition, we also introduced an additional extra-small object feature extractor and detector. Experimental results in a custom IR dataset with multiple classes (birds, drones, planes, and helicopters) demonstrate that GAANet shows improvement compared to state-of-the-art detectors. In comparison to GhostNet-YOLOv5, GAANet has higher overall mean average precision (mAP@50), recall, and precision around 2.5%, 2.3%, and 1.4%, respectively. The dataset and code for this paper are available as open source at https://github.com/ZeeshanKaleem/GhostAutoAnchorNet.