CAST-YOLO: An Improved YOLO Based on a Cross-Attention Strategy Transformer for Foggy Weather Adaptive Detection

Liu, Xinyi; Zhang, Baofeng; Liu, Na

doi:10.3390/app13021176

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…Self-Attention principally focuses on the dependence relations in the input sequence to pick up the modelling capacity sequence data. Xue et al 26 27 introduced an improved YOLO model based on cross-attention strategy transformer, ensuring better robustness for models with noise input. To sum up, small target detection has been a challenging problem in the YOLO series algorithm.…”

Section: Attention Mechanism Modelmentioning

confidence: 99%

Metal surface defect detection based on improved YOLOv5

Zhou,

Lu,

et al. 2023

Sci Rep

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During the production of metal material, various complex defects may come into being on the surface, together with large amount of background texture information, causing false or missing detection in the process of small defect detection. To resolve those problems, this paper introduces a new model which combines the advantages of CSPlayer module and Global Attention Enhancement Mechanism based on the YOLOv5s model. First of all, we replace C3 module with CSPlayer module to augment the neural network model, so as to improve its flexibility and adaptability. Then, we introduce the Global Attention Mechanism (GAM) and build the generalized additive model. In the meanwhile, the attention weights of all dimensions are weighted and averaged as output to promote the detection speed and accuracy. The results of the experiment in which the GC10-DET augmented dataset is involved, show that the improved algorithm model performs better than YOLOv5s in precision, mAP@0.5 and mAP@0.5: 0.95 by 5.3%, 1.4% and 1.7% respectively, and it also has a higher reasoning speed.

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Section: Attention Mechanism Modelmentioning

confidence: 99%

Metal surface defect detection based on improved YOLOv5

Zhou,

Lu,

et al. 2023

Sci Rep

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“…Road scene object detection presents a challenging domain where trade-offs must be made between the strengths and weaknesses of different models to meet the requirements of real-time performance, accuracy, and robustness [39]. A comparison of the pros and cons of one-stage object detection algorithms is presented in Table 2.…”

Section: Research On One-stage Approaches In Object Detectionmentioning

confidence: 99%

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Liu,

Lin

2023

Electronics

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Intelligent traffic systems represent one of the crucial domains in today’s world, aiming to enhance traffic management efficiency and road safety. However, current intelligent traffic systems still face various challenges, particularly in the realm of target detection. These challenges include adapting to complex traffic scenarios and the lack of precise detection for multiple objects. To address these issues, we propose an innovative approach known as YOLOv8-SnakeVision. This method introduces Dynamic Snake Convolution, Context Aggregation Attention Mechanisms, and the Wise-IoU strategy within the YOLOv8 framework to enhance target detection performance. Dynamic Snake Convolution assists in accurately capturing complex object shapes and features, especially in cases of target occlusion or overlap. The Context Aggregation Attention Mechanisms allow the model to better focus on critical image regions and effectively integrate information, thus improving its ability to recognize obscured targets, small objects, and complex patterns. The Wise-IoU strategy combines dynamic non-monotonic focusing mechanisms, aiming to more precisely regress target bounding boxes, particularly for low-quality examples. We validate our approach on the BDD100K and NEXET datasets. Experimental results demonstrate that YOLOv8-SnakeVision excels in various complex road traffic scenarios. It not only enhances small object detection but also strengthens the ability to recognize multiple targets. This innovative method provides robust support for the development of intelligent traffic systems and holds the promise of achieving further breakthroughs in future applications.

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“…Hsu et al [16] employed FCOS [8] as a detector and proposed the centreaware feature alignment method. Liu et al [36] proposed a cross-attention strategy within a teacher-student framework to facilitate knowledge transfer in a yolov5 detector. Recently, the cross-domain Transformer method has gained attention in the field.…”

Section: Cross-domain Object Detectionmentioning

confidence: 99%

“…Liu et al. [36] proposed a cross‐attention strategy within a teacher‐student framework to facilitate knowledge transfer in a yolov5 detector. Recently, the cross‐domain Transformer method has gained attention in the field.…”

Section: Related Workmentioning

confidence: 99%

Category‐related attention domain adaptation for one‐stage cross‐domain object detection

Guan,

Dong,

Gao

et al. 2023

IET Image Processing

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Cross‐domain object detection aims to generalize the distribution of features extracted by an object detector from an annotated domain to an unknown and unlabelled domain. Although one‐stage cross‐domain object detectors have significant advantages in deployment than two‐stage ones, they suffer from two problems. First, neglect of category features and inaccurate alignment between multiple category features would lead to decreased domain adaptation efficiency. Second, one‐stage detectors are more sensitive to imbalance of samples and negative samples severely affect the alignment process of domain adaptation. To overcome these two problems, an innovative category‐related attention domain adaptive method that refines discrimination for each category's feature has been proposed in this paper. In the proposed method, a group of domain discriminators is assigned to each category to refine the fine‐grained features between categories. The discriminators are trained via an adversarial discriminant framework to align the fine‐grained distributions cross different domains. A category attention alignment (CAA) module is proposed to navigate more attention to the foreground regions in instance‐level, which effectively alleviates the negative migration problem caused by the positive and negative sample imbalance of the one‐stage detector. Specifically, two sub‐modules in the CAA module are developed: a local CAA module and a global CAA module. These modules aim to optimize the domain offsets in both the local and global dimensions. In addition, a progressive global alignment module is designed to align image‐level features, offering prior knowledge of migration for the CAA module. The progressive global alignment module and CAA module collaboratively engage in benign competition with the backbone network across various levels. Extensive transferring experiments are conducted among cityscapes, foggy cityscapes, SIM10K, and KITTI. Experimental results show that the proposed method has much superior performance than other one‐stage cross‐domain detectors.

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CAST-YOLO: An Improved YOLO Based on a Cross-Attention Strategy Transformer for Foggy Weather Adaptive Detection

Cited by 11 publications

References 18 publications

Metal surface defect detection based on improved YOLOv5

Metal surface defect detection based on improved YOLOv5

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Category‐related attention domain adaptation for one‐stage cross‐domain object detection

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