NextDet: Efficient Sparse-to-Dense Object Detection with Attentive Feature Aggregation

Priyank Kalgaonkar and Mohamed El-Sharkawy ()
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Priyank Kalgaonkar: Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology Indianapolis, Indianapolis, IN 46254, USA
Mohamed El-Sharkawy: Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology Indianapolis, Indianapolis, IN 46254, USA

Future Internet, 2022, vol. 14, issue 12, 1-16

Abstract: Object detection is a computer vision task of detecting instances of objects of a certain class, identifying types of objects, determining its location, and accurately labelling them in an input image or a video. The scope of the work presented within this paper proposes a modern object detection network called NextDet to efficiently detect objects of multiple classes which utilizes CondenseNeXt, an award-winning lightweight image classification convolutional neural network algorithm with reduced number of FLOPs and parameters as the backbone, to efficiently extract and aggregate image features at different granularities in addition to other novel and modified strategies such as attentive feature aggregation in the head, to perform object detection and draw bounding boxes around the detected objects. Extensive experiments and ablation tests, as outlined in this paper, are performed on Argoverse-HD and COCO datasets, which provide numerous temporarily sparse to dense annotated images, demonstrate that the proposed object detection algorithm with CondenseNeXt as the backbone result in an increase in mean Average Precision (mAP) performance and interpretability on Argoverse-HD’s monocular ego-vehicle camera captured scenarios by up to 17.39% as well as COCO’s large set of images of everyday scenes of real-world common objects by up to 14.62%.

Keywords: CondenseNeXt; object detection; PyTorch; deep learning; convolutional neural network (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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