Sudip Chakrabarty

Sourov Roy Shuvo, Prajwal Panth, Rajesh Chowdhury et al.

In modern warfare, drones are becoming an essential part of intelligence gathering and carrying out precise attacks in different kinds of hostile environments. Their ability to operate in real-time and hostile environments from a safe distance makes them invaluable for surveillance and military operations. The KIIT-MiTA dataset is comprised of images of different military scenarios taken from drones, and these provide a foundation for detecting military objects, but it does not take into account the various types of real-world scenarios. With that in mind, to evaluate how the models are performing under varying conditions, four different types of datasets are created: Gray Scale, Thermal Vision, Night Vision, and Obscura Vision. These simulate the real-world environments such as low visibility, heat-based imagery, and nighttime conditions. The YOLOv11-small model is trained and used to detect objects across diverse settings. This research boosts the performance and reliability of drone-based operations by contributing to the development of advanced detection systems in both defensive and offensive missions.

Rajdeep Chatterjee, Sudip Chakrabarty, Trishaani Acharjee et al.

Unmanned aerial vehicles (UAVs), commonly known as drones, are increasingly used across diverse domains, including logistics, agriculture, surveillance, and defense. While these systems provide numerous benefits, their misuse raises safety and security concerns, making effective detection mechanisms essential. Acoustic sensing offers a low-cost and non-intrusive alternative to vision or radar-based detection, as drone propellers generate distinctive sound patterns. This study introduces AUDRON (AUdio-based Drone Recognition Network), a hybrid deep learning framework for drone sound detection, employing a combination of Mel-Frequency Cepstral Coefficients (MFCC), Short-Time Fourier Transform (STFT) spectrograms processed with convolutional neural networks (CNNs), recurrent layers for temporal modeling, and autoencoder-based representations. Feature-level fusion integrates complementary information before classification. Experimental evaluation demonstrates that AUDRON effectively differentiates drone acoustic signatures from background noise, achieving high accuracy while maintaining generalizability across varying conditions. AUDRON achieves 98.51 percent and 97.11 percent accuracy in binary and multiclass classification. The results highlight the advantage of combining multiple feature representations with deep learning for reliable acoustic drone detection, suggesting the framework's potential for deployment in security and surveillance applications where visual or radar sensing may be limited.

Sudip Chakrabarty

The "You Only Look Once" (YOLO) framework has long served as the benchmark for real-time object detection, yet traditional iterations (YOLOv1 through YOLO11) remain constrained by the latency and hyperparameter sensitivity of Non-Maximum Suppression (NMS) post-processing. This paper analyzes a comprehensive analysis of YOLO26, an architecture that fundamentally redefines this paradigm by eliminating NMS in favor of a native end-to-end learning strategy. This study examines the critical innovations that enable this transition, specifically the introduction of the MuSGD optimizer for stabilizing lightweight backbones, STAL for small-target-aware assignment, and ProgLoss for dynamic supervision. Through a systematic review of official performance benchmarks, the results demonstrate that YOLO26 establishes a new Pareto front, outperforming a comprehensive suite of predecessors and state-of-the-art competitors (including RTMDet and DAMO-YOLO) in both inference speed and detection accuracy. The analysis confirms that by decoupling representation learning from heuristic post-processing, YOLOv26 successfully resolves the historical trade-off between latency and precision, signaling the next evolutionary step in edge-based computer vision.

Sudip Chakrabarty, Pappu Bishwas, Rajdeep Chatterjee et al.

Environmental sound classification is a field of growing importance for urban monitoring and cultural soundscape analysis, especially within the acoustically rich environments of South Asia. These regions present a unique challenge as multiple natural, human, and cultural sounds often overlap, straining traditional methods that frequently rely on Mel Frequency Cepstral Coefficients (MFCC). This study introduces a novel spectrogram-based methodology with a superior ability to capture these complex auditory patterns. A Convolutional Neural Network (CNN) architecture is implemented to solve a demanding multilabel, multiclass classification problem on the SAS-KIIT dataset. To demonstrate robustness and comparability, the approach is also validated using the renowned UrbanSound8K dataset. The results confirm that the proposed spectrogram-based method significantly outperforms existing MFCC-based techniques, achieving higher classification accuracy across both datasets. This improvement lays the groundwork for more robust and accurate audio classification systems in real-world applications.