Object detection based on polarization image fusion and grouped convolutional attention network

Tan, Ailing; Guo, Tong; Zhao, Yongsheng; Wang, Yunxin; Li, Xiaohang

doi:10.1007/s00371-023-03022-6

Cited by 6 publications

(1 citation statement)

References 31 publications

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“…Compared with its predecessor, YOLOv4 [26], the model has been optimized in various aspects such as architecture, training approach, and performance. The core architecture of YOLOv5 relies on Focus and CSPDarknet53 [27] to minimize redundant calculations and optimize performance. In 2022, Wang et al introduced the more advanced YOLOv7.…”

Section: The Yolo Familymentioning

confidence: 99%

SOCR‐YOLO: Small Objects Detection Algorithm in Medical Images

Liu,

Li,

Jiang

et al. 2024

Int J Imaging Syst Tech

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In the field of medical image analysis, object detection plays a crucial role by providing interpretable diagnostic information to healthcare professionals. Although current object detection models have achieved remarkable success in conventional images, their performance in detecting abnormalities in medical images has not been as satisfactory. This is primarily due to the complexity of anatomical structures in medical images, and the fact that some lesions may have subtle features, particularly in the case of early‐stage, small‐scale abnormalities. To address this challenge, we introduce SOCR‐YOLO, a novel lesion detection model with online convolutional reparameterization based on channel shuffling. First, it employs the SOCR (Shuffled Channel with Online Convolutional Re‐parameterization) module to establish a connection between feature concatenation and computational efficiency, aiming to extract more comprehensive information while reducing time consumption. Second, it incorporates the Bi‐FPN structure to achieve multiscale feature fusion. Lastly, the loss function has been optimized to improve the model training process. We evaluated two datasets, chest x‐ray (Vindr‐CXR) and brain tumor (Br35H), provided by the Kaggle competition. Experimental results show that the proposed method has outperformed several state‐of‐the‐art models, including YOLOv8, YOLO‐NAS, and RT‐DETR, in both speed and accuracy. Notably, in the context of chest x‐ray anomaly detection, SOCR‐YOLO exhibits a 1.8% enhancement in accuracy over YOLOv8 while simultaneously reducing floating‐point operations by 26.3%. Additionally, a similar 1.8% improvement in accuracy is observed in the detection of brain tumors. The results indicate the superior ability of our model to detect multiscale variations and small lesions.

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Section: The Yolo Familymentioning

confidence: 99%