Efficient three-dimensional point cloud object detection based on improved Complex-YOLO

Yongxin, Shao,; Zhetao, Sun,; Tan, Anhui; Yan, Tianhong

doi:10.3389/fnbot.2023.1092564

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…Unlike traditional models which might rely on a combination of data types, Complex-YOLO stands out as it operates directly on point cloud data. Furthermore, its adaptability extends to semantic point cloud data, both of which are generated by 3D LiDAR systems [41]. These synergies between deep learning and 3D LiDAR have paved the way for more sophisticated and accurate people identification solutions.…”

Section: B Related Workmentioning

confidence: 99%

People Identification in Private Car Using 3D LiDAR With Generative Image Inpainting and YOLOv5

Shao,

Bouazizi,

Meng

et al. 2024

IEEE Access

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People Identification is a critical aspect in developing modern vehicles, aimed at enhancing safety and comfort levels. Most traditional methods of people identification in vehicles use RGB images or videos. In this study, we introduce a novel methodology for identifying individuals in private car scenarios, utilizing 3D Light Detection and Ranging (LiDAR) technology, generative image inpainting based on Contextual Attention, and the YOLOv5 model. Initially, we gather data utilizing a 3D-LiDAR instrument and subsequently convert the acquired depth data into depth images. Following this, the depth images are annotated manually to indicate the positions and identifiers of various individuals occupying distinct seats. This annotated data serves as the training material for the YOLOv5 model, facilitating the recognition and categorization of subjects. However, given that individuals seated in the back often have parts of their bodies occluded by the front seats and the passengers in them, we employ generative image inpainting techniques to reveal the occluded portions. This step significantly enhances the precision in detecting and identifying individuals situated in the back seats. We implemented our strategy on a restricted group of four participants, conducting training and testing phases within identical environments. Prior to the inpainting process, the classification's F1 score stood at 66.5%. After inpainting, we observed a notable surge in the F1 score for the rear-seat passengers increased by 17.1%.

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Section: B Related Workmentioning

confidence: 99%

People Identification in Private Car Using 3D LiDAR With Generative Image Inpainting and YOLOv5

Shao,

Bouazizi,

Meng

et al. 2024

IEEE Access

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show abstract

“…It is difficult to perceive the multi-scale fishing vessel target better using the unified attention mechanism, and it will be interfered with by non-correlated information. The second class of methods uses better-performance backbone network-optimized target detection algorithms, such as ST-YOLOA [4], and GGT-YOLO [5] with PAG-YOLO [6]. The role of the backbone network in the target detection algorithm is to extract and compress the features such as shape contour, position, and hull color of the target instance in the RGB image.…”

Section: Introductionmentioning

confidence: 99%

Adaptive spatial attention dual-branch fishing boat detection network

Yang,

Liu

2024

J. Phys.: Conf. Ser.

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Aiming at the harbor environment, the target detection accuracy of fishing vessels is low, and it is prone to the problems of fishing vessel misdetection and omission detection. In this paper, we propose a fishing vessel target detection algorithm called ASDNet based on YOLOX. Firstly, an Adaptive Spatial Attention Module (ASAM) was designed and used to improve the detection of fishing vessel targets; secondly, a two-branch backbone network was designed for multidimensional fishing vessel feature extraction. Meanwhile, a bilateral enhanced fusion strategy (BFFS) is designed to fuse the branch features to improve the characterization ability of the network; finally, the loss function is improved by introducing the Focal-CIOU loss bounding box loss function to reduce the effects of the detection position deviation of the fishing vessel target and the overlap of the vessel hull to improve the detection performance. The above methods are validated using the homemade fishing vessel dataset, and the results show that the precision rate (P) and recall rate (R) are greatly improved. The average precision rate (mAP@50-95) value reaches 80.25%, which is 2.39% higher than that of the 77.86% of the YOLOX. It significantly improves the precision of the detection, meets the requirements of the performance of the target detection of the fishing vessel, and has certain practical significance in engineering.

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MS23D: A 3D object detection method using multi-scale semantic feature points to construct 3D feature layer

Shao,

Tan,

Wang

et al. 2024

Neural Networks

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Efficient three-dimensional point cloud object detection based on improved Complex-YOLO

Cited by 5 publications

References 37 publications

People Identification in Private Car Using 3D LiDAR With Generative Image Inpainting and YOLOv5

People Identification in Private Car Using 3D LiDAR With Generative Image Inpainting and YOLOv5

Adaptive spatial attention dual-branch fishing boat detection network

MS23D: A 3D object detection method using multi-scale semantic feature points to construct 3D feature layer

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