SOLARNet: A single stage regression based framework for efficient and robust object recognition in aerial images

Object recognition and localization play a crucial role in aerial images and their applications. The aerial images are challenging due to the large aspect ratio, arbitrary orientation, variation in scales, and non-uniform and cluttered object distribution. To address these challenges, we propose an efficient and robust model called the Simultaneous Object Localization and Recognition Network (SOLARNet), which is a fu-sion network that integrates two different sub-networks: PixelAttentionDetector (PD) and RotationDetec-tor (RD). The PD considers features from different scales and cluttered objects, while RD handles rotation invariance, giving horizontal and oriented object detection results. The state-of-the-art model fails to im-prove accuracy when images are adversarially attacked. SOLARNet is not only efficient in terms of accu-racy but also robust concerning to Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Jaco-bian Based Saliency Map (JSM) adversarial attacks, which is the crucial factor for any mission-critical sys-tem. We have performed experiments and achieved the accuracy on the publicly available DOTA dataset (75.00% mAP, 66.40% mAP) and DIOR dataset (88.60% mAP, 81.50% mAP) for horizontal and oriented ob-ject recognition tasks respectively while having high inference speed. Qualitative and quantitative results reported in this paper substantiate the superiority of SOLARNet over other state-of-the-art methodologies. & COPY; 2023 Elsevier B.V. All rights reserved.