Single-fusion detector: Towards faster multi-scale object detection

• Main Author: Antioquia, Arren Matthew C.
• Format: text
• Language: English
• Published: Animo Repository 2019
• Subjects: Image converters; Neural networks (Computer science); Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/etd_masteral/6609; https://animorepository.dlsu.edu.ph/context/etd_masteral/article/13597/viewcontent/Antioquia__Arren_Matthew_Capuchino2___Single_Fusion_Detector_Towards_Faster_Multi_Scale_Object_Detection___ANTIOQUIA_Redacted.pdf
• Institution: De La Salle University

Despite recent improvements, the arbitrary sizes of objects still impede the predictive ability of object detectors. Recent solutions combine feature maps of different receptive fields to detect multi-scale objects. However, these methods have large computational costs resulting to slower inference time, which is not practical for real-time applications. Contrarily, fusion methods depending on large networks with many skip connections require larger memory footprint, prohibiting usage in devices with limited memory. In this paper, we propose a simpler novel fusion method which integrates multiple feature maps using a single concatenation operation. Our method can flexibly adapt to any base network, allowing for tailored performance for different computational requirements. Our approach achieves 81.7% mAP at 41 FPS on the PASCAL VOC dataset using ResNet-50 as the base network, which is superior in terms of both speed and mAP as compared to several state-of-the-art baselines that uses larger base networks.