Abstract
Aerial images usually are huge (around 2K resolution). Such high-resolution images contain thousands of small objects, and detecting all of them is a very challenging problem. The complexity of detection and classification in real-time is much higher than the usual images (<1K with high Object to Image Ratio OIR). Deep learning has many algorithms for object detection, but they are not designed for handling aerial images, and these algorithms are often sub-optimal for small scale object detection and their precise localization. In this work, a novel technique based on a modified SSD architecture OIR-SSD has proposed for real-time object detection on aerial images attaining high mean Average Precision (mAP). OIR-SSD has two approaches. The approach-I proposed for higher mAP, whereas the approach-II proposed to achieve real-time object detection. The approach-I has improved mAP from 0.72 to 0.92 (28% improvement) on Stanford data-set while from 0.04 to 0.44 (1100% improvement) on Visedrone2018 at 4 Frames Per Second (FPS) whereas the approach-II has improved mAP from 0.72 to 0.82 at 42 FPS.
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Sharma, R., Pandey, R., Nigam, A. (2019). Real Time Object Detection on Aerial Imagery. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11678. Springer, Cham. https://doi.org/10.1007/978-3-030-29888-3_39
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