Singular and Multimodal Techniques of 3D Object Detection: Constraints, Advancements and Research Direction

Karim, Tajbia; Mahayuddin, Zainal Rasyid; Hasan, Mohammad Kamrul

doi:10.3390/app132413267

Cited by 3 publications

(1 citation statement)

References 131 publications

(122 reference statements)

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“…Furthermore, the region proposal-free approach introduces an important innovation by treating object detection as a direct-regression problem or keypoint prediction [ 35 , 36 , 37 , 38 ]. As real-world applications increasingly require real-time processing [ 39 , 40 , 41 , 42 , 43 ], the importance of region proposal-free detectors such as the proposed PF-FCN becomes more remarkable. In summary, from region proposal-based detectors to region proposal-free detectors, the research emphasis is always on achieving a delicate balance between accuracy and efficiency.…”

Section: Related Workmentioning

confidence: 99%

Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map

Su,

Adam,

Nasrudin

et al. 2024

Sensors

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Region proposal-based detectors, such as Region-Convolutional Neural Networks (R-CNNs), Fast R-CNNs, Faster R-CNNs, and Region-Based Fully Convolutional Networks (R-FCNs), employ a two-stage process involving region proposal generation followed by classification. This approach is effective but computationally intensive and typically slower than proposal-free methods. Therefore, region proposal-free detectors are becoming popular to balance accuracy and speed. This paper proposes a proposal-free, fully convolutional network (PF-FCN) that outperforms other state-of-the-art, proposal-free methods. Unlike traditional region proposal-free methods, PF-FCN can generate a “box map” based on regression training techniques. This box map comprises a set of vectors, each designed to produce bounding boxes corresponding to the positions of objects in the input image. The channel and spatial contextualized sub-network are further designed to learn a “box map”. In comparison to renowned proposal-free detectors such as CornerNet, CenterNet, and You Look Only Once (YOLO), PF-FCN utilizes a fully convolutional, single-pass method. By reducing the need for fully connected layers and filtering center points, the method considerably reduces the number of trained parameters and optimizes the scalability across varying input sizes. Evaluations of benchmark datasets suggest the effectiveness of PF-FCN: the proposed model achieved an mAP of 89.6% on PASCAL VOC 2012 and 71.7% on MS COCO, which are higher than those of the baseline Fully Convolutional One-Stage Detector (FCOS) and other classical proposal-free detectors. The results prove the significance of proposal-free detectors in both practical applications and future research.

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Section: Related Workmentioning

confidence: 99%

Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map

Su,

Adam,

Nasrudin

et al. 2024

Sensors

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Enhancing 3D object detection in autonomous vehicles based on synthetic virtual environment analysis

Li,

Siniosoglou,

Karamitsou

et al. 2025

Image and Vision Computing

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Real-Time Multimodal 3D Object Detection with Transformers

Liu,

Duan

2024

WEVJ

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The accuracy and real-time performance of 3D object detection are key factors limiting its widespread application. While cameras capture detailed color and texture features, they lack depth information compared to LiDAR. Multimodal detection combining both can improve results but incurs significant computational overhead, affecting real-time performance. To address these challenges, this paper presents a real-time multimodal fusion model called Fast Transfusion that combines the benefits of LiDAR and camera sensors and reduces the computational burden of their fusion. Specifically, our Fast Transfusion method uses QConv (Quick Convolution) to replace the convolutional backbones compared to other models. QConv concentrates the convolution operations at the feature map center, where the most information resides, to expedite inference. It also utilizes deformable convolution to better match the actual shapes of detected objects, enhancing accuracy. And the model incorporates EH Decoder (Efficient and Hybrid Decoder) which decouples multiscale fusion into intra-scale interaction and cross-scale fusion, efficiently decoding and integrating features extracted from multimodal data. Furthermore, our proposed semi-dynamic query selection refines the initialization of object queries. On the KITTI 3D object detection dataset, our proposed approach reduced the inference time by 36 ms and improved 3D AP by 1.81% compared to state-of-the-art methods.

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Singular and Multimodal Techniques of 3D Object Detection: Constraints, Advancements and Research Direction

Cited by 3 publications

References 131 publications

Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map

Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map

Enhancing 3D object detection in autonomous vehicles based on synthetic virtual environment analysis

Real-Time Multimodal 3D Object Detection with Transformers

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