To facilitate the development of intelligent unmanned loaders and improve the recognition accuracy of loaders in complex scenes, we propose a construction machinery and material target detection algorithm incorporating an attention mechanism (AM) to improve YOLOv4-Tiny. First, to ensure the robustness of the proposed algorithm, we adopt style migration and sliding window segmentation to increase the underlying dataset’s diversity. Second, to address the problem that YOLOv4-Tiny’s (the base network) framework only adopts a layer-by-layer connection form, which demonstrates an insufficient feature extraction ability, we adopt a multilayer cascaded residual module to deeply connect low- and high-level information. Finally, to filter redundant feature information and make the proposed algorithm focus more on important feature information, a channel AM is added to the base network to perform a secondary screening of feature information in the region of interest, which effectively improves the detection accuracy. In addition, to achieve small-scale object detection, a multiscale feature pyramid network structure is employed in the prediction module of the proposed algorithm to output two prediction networks with different scale sizes. The experimental results show that, compared with the traditional network structure, the proposed algorithm fully incorporates the advantages of residual networks and AM, which effectively improves its feature extraction ability and recognition accuracy of targets at different scales. The final proposed algorithm exhibits the features of high recognition accuracy and fast recognition speed, with mean average precision and detection speed reaching 96.82% and 134.4 fps, respectively.
In order to improve the recognition accuracy of construction machinery and equipment and materials in low contrast scenes, a construction machinery material recognition algorithm based on multisource sensor information fusion is proposed. In the paper, the millimeter wave radar is fused with the camera considering its strong penetration ability in rainy and foggy days and dim environments. Firstly, the spatial coordinates of radar and camera are unified by establishing a spatial fusion model of millimeter wave and camera; then the target acquired by millimeter wave is projected onto the image and the detection frame intersection and ratio model is used to generate the region of interest of the camera; finally, the improved YOLOv2 algorithm is used to identify the region of interest, and in the improved idea, the low‐level information is first connected with the high‐level information in multilayer depth. At the same time, a multiscale feature pyramid network structure is used to achieve recognition of objects of different scales. This model effectively reduces interference from other feature categories while improving the recognition efficiency of the system. The algorithm can effectively improve the recognition accuracy of mechanical materials in low‐contrast scenes, as demonstrated by the validation of different scenes.
To improve the accuracy of material identification under low contrast conditions, this paper proposes an improved YOLOv4-tiny target detection method based on an adaptive self-order piecewise enhancement and multiscale feature optimization. The model first constructs an adaptive self-rank piecewise enhancement algorithm to enhance low-contrast images and then considers the fast detection ability of the YOLOv4-tiny network. To make the detection network have a higher accuracy, this paper adds an SE channel attention mechanism and an SPP module to this lightweight backbone network to increase the receptive field of the model and enrich the expression ability of the feature map. The network can pay more attention to salient information, suppress edge information, and effectively improve the training accuracy of the model. At the same time, to better fuse the features of different scales, the FPN multiscale feature fusion structure is redesigned to strengthen the fusion of semantic information at all levels of the network, enhance the ability of network feature extraction, and improve the overall detection accuracy of the model. The experimental results show that compared with the mainstream network framework, the improved YOLOv4-tiny network in this paper effectively improves the running speed and target detection accuracy of the model, and its mAP index reaches 98.85%, achieving better detection results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.