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This content will become publicly available on October 28, 2024

Title: Sniffer Faster R-CNN ++: An Efficient Camera-LiDAR Object Detector with Proposal Refinement on Fused Candidates

In this paper we present Sniffer Faster R-CNN++, an efficient Camera-LiDAR late fusion network for low complexity and accurate object detection in autonomous driving scenarios. The proposed detection network architecture operates on output candidates of any 3D detector and proposals from regional proposal network of any 2D detector to generate final prediction results. In comparison to the single modality object detection approaches, fusion based methods in many instances suffer from dissimilar data integration difficulties. On one hand, fusion based network models are complicated in nature and on the other hand they require large computational overhead and resources, processing pipelines for training and inference specially, the early fusion and deep fusion approaches. As such, we devise a late fusion network that in-cooperates pre-trained, single-modality detectors without change, performing association only at the detection level. In addition to this, lidar based method fail to detect distant object due to its sparse nature so we devise proposal refinement algorithm to jointly optimize detection candidates and assist detection for distant objects. Extensive experiments on both the 3D and 2D detection benchmark of challenging KITTI dataset illustrate that our proposed network architecture significantly improves the detection accuracy, accelerating the detection speed.

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Award ID(s):
2231519 1852134
NSF-PAR ID:
10492601
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
ACM Journal on Autonomous Transportation Systems
ISSN:
2833-0528
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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