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Path Aggregation Network based WATT-EffNet for Unmanned Aerial Image Classification

International Journal of Electronics and Communication Engineering
© 2025 by SSRG - IJECE Journal
Volume 12 Issue 9
Year of Publication : 2025
Authors : Nakkala Geetha, Gurram Sunitha
10.14445/23488549/IJECE-V12I9P109
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How to Cite?

Nakkala Geetha, Gurram Sunitha, "Path Aggregation Network based WATT-EffNet for Unmanned Aerial Image Classification," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 9, pp. 108-117, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I9P109

Abstract:

High-resolution aerial images captured by UAVs are critical in various real-world applications. However, effective classification of these images is challenging due to scale variation, occlusions, and complex scene structures. Existing deep learning models often face a trade-off between computational efficiency and classification accuracy. To address this issue, a novel Path Aggregation Network-based Wider Attention EfficientNet (PANet-WATT-EffNet) is proposed. PANet-WATT-EffNet employs EfficientNet as a lightweight backbone, combined with wider attention layers to capture salient regions. PANet is used for multi-scale feature fusion. PANet-WATT-EffNet’s design improves the extraction of fine-grained and global features, enabling accurate recognition of small and complex objects in aerial imagery. Experimental evaluation on UAV benchmark datasets shows that the model achieves 97.71% accuracy. A significant gain is observed in F-measure, MCC, and reduced RMSE values of PANet-WATT-EffNet, while also lowering computational time compared to existing methods. The results confirm the robustness of the approach in handling diverse aerial imagery. The lightweight architecture further supports deployment on resource-constrained edge devices, making it suitable for applications in precision agriculture, urban infrastructure monitoring, disaster management, and defence surveillance.

Keywords:

Remote sensing, Path aggregation network, EfficientNet, Multi-scale feature fusion, Remote sensing, Lightweight deep learning.

References:

[1] Emmanuel K. Raptis et al., “End-to-End Precision Agriculture UAV-based Functionalities Tailored to Field Characteristics,” Journal of Intelligent & Robotic Systems, vol. 107, pp. 1-26, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Peter B. Boucher et al., “Flying High: Sampling Savanna Vegetation with UAV‐Lidar,” Methods in Ecology and Evolution, vol. 14, no. 7, pp. 1668-1686, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Thaísa F. Bergamo et al., “From UAV to PlanetScope: Upscaling Fractional Cover of an Invasive Species Rosa Rugosa,” Journal of Environmental Management, vol. 336, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Chenglong Zhang et al., “Feasibility Assessment of Tree-Level Flower Intensity Quantification from UAV RGB Imagery: A Triennial Study in an Apple Orchard,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 197, pp. 256-273, 2023. [CrossRef] [Google Scholar] [Publisher Link]
[5] Parthasarathy Velusamy et al., “Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges,” Energies, vol. 15, no. 1, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Jean-Paul Yaacoub et al., “Security Analysis of Drones Systems: Attacks, Limitations, and Recommendations,” Internet of Things, vol. 11, pp. 1-39, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Chrysanthos Maraveas, “Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art,” Applied Sciences, vol. 13, no. 1, pp. 1-35, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Mingxing Tan, and Quoc Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” Proceedings of 36th International Conference on Machine Learning (ICML), vol. 97, pp. 6105-6114, 2019.
[Google Scholar] [Publisher Link]
[9] Gao Yu Lee et al., “WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images,” IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Shu Liu et al., “Path Aggregation Network for Instance Segmentation,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 8759-8768, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Nicholus Mboga et al., “Fully Convolutional Networks for Land Cover Classification from Historical Panchromatic Aerial Photographs,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 167, pp. 385-395, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Gab Su Moon, Kyoung Seop Kim, and Yun Jea Choung, “Land Cover Classification based on High Resolution KOMPSAT-3 Satellite Imagery using Deep Neural Network Model,” Journal of the Korean Association of Geographic Information Studies, vol. 23, no. 3, pp. 252-262, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Wei Meng, and Meng Tia, “Unmanned Aerial Vehicle Classification and Detection Based on Deep Transfer Learning,” 2020 International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), Sanya, China, pp. 280-285, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Nidhish Dubey, Nanduri Mahathi Sai Nithin, and Shrivishal Tripathi, “Analysis and Comparison of Image-Based UAV Detection and Identification,” 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Prayagraj, India, pp. 1-6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Ahmet Furkan Büyükkelek et al., “Image Processing Based Transportation in Unmanned Aerial Vehicles,” 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey, pp. 1-4, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Linhuan Jiang, Zhen Zhang, and Yabin Hu, “Unmanned Aerial Vehicle Hyperspectral Remote Sensing Image Classification Based on Parallel Convolutional Neural Network,” 2023 11th International Conference on Information Systems and Computing Technology (ISCTech), Qingdao, China, pp. 526-530, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] David Herrera et al., “Combining Image Classification and Unmanned Aerial Vehicles to Estimate the State of Explorer Roses,” AgriEngineering, vol. 6, no. 2, pp. 1008-1021, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Shuangshuang Li, and Wenming Cao, “SEMPANet: A Modified Path Aggregation Network with Squeeze-Excitation for Scene Text Detection,” Sensors, vol. 21, no. 8, pp. 1-16, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Yunzuo Zhang et al., “Full-Scale Feature Aggregation and Grouping Feature Reconstruction-based UAV Image Target Detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Sovit Ranjan Rath, UAV - Small Object Detection Dataset, Kaggle, 2023. [Online]. Available: https://www.kaggle.com/datasets/sovitrath/uav-small-object-detection-dataset
[21] Weihancug, 10-Category UAV Small-Weak Object Detection Dataset (UAVOD10), GitHub Repository, 2022. [Online]. Available: https://github.com/weihancug/10-category-UAV-small-weak-object-detection-dataset-UAVOD10
[22] Uddhav Bhattarai et al., “Automatic Blossom Detection in Apple Trees using Deep Learning,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 15810-15815, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Gyanendra Prasad Joshi et al., “Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks,” Mathematics, vol. 9, no. 22, pp. 1-17, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Chenxiao Zhang et al., “A Multi-Level Context-Guided Classification Method with Object-Based Convolutional Neural Network for Land Cover Classification using Very High Resolution Remote Sensing Images,” International Journal of Applied Earth Observation and Geoinformation, vol. 88, pp. 1-13, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Yue Xi et al., “DRL-GAN: Dual-Stream Representation Learning GAN for Low-Resolution Image Classification in UAV Applications,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 1705-1716, 2021.
[CrossRef] [Google Scholar] [Publisher Link]