Dilated convolution based RCNN using feature fusion for Low-Altitude aerial objects. (1st August 2022)

• Record Type: Journal Article
• Authors: Mittal, Payal; Sharma, Akashdeep; Singh, Raman; Dhull, Vishal
• Is Part Of: Expert systems with applications. Volume 199(2022)
• Publication Date: 2022-08-01
• Subjects: Region proposal -- Feature fusion -- Dilated convolutions -- Receptive field -- Object detection; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117106
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: The low-altitude aerial objects are hard to detect by existing deep learning-based object detectors because of the scale variance, small size, and occlusion-related problems. Deep learning-based detectors do not consider contextual information about the scale information of small-sized objects in low-altitude aerial images. This paper proposes a new system using the concept of receptive fields and fusion of feature maps to improve the efficiency of deep object detectors in low-altitude aerial images. A Dilated ResNet Module (DRM) is proposed, motivated from the trident networks, which works on dilated convolutions to study the contextual data for specifically small-sized objects. Applicability of this component builds the model strong towards scale variations in low-altitude aerial objects. Then, Feature Fusion Module (FFM) is created to offer semantic intelligence for better detection of low-altitude aerial objects. We have chosen vastly deployed faster RCNN as the base detector for the proposal of our technique. The dilated convolution-based RCNN using feature fusion (DCRFF) system is implemented on a benchmark low-altitude UAV based-object detection dataset, VisDrone, which contains multiple object categories of pedestrians, vehicles in crowded scenes. The experiments exhibit the enactment of the given detector on chosen low-altitude aerial object dataset. The proposed system of DCRFF achieves 35.04% mAP on the challenging VisDrone dataset, indicating an average improvement of 2% when compared.