DSConv: Efficient Convolution Operator

Gennari, Marcelo; Fawcett, Roger; Prisacariu, Victor Adrian

doi:10.48550/arxiv.1901.01928

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…From table 2, it can be seen that OD-YOLO is optimal compared with other models in recall, map 50 and GFLOPs. Compared to the baseline model, OD-YOLO improves precision by 4.8%, recall by 2.4%, map 50 by 2.2%, and the computational complexity is reduced by 3.7; compared with other improved convolutional models, OD-YOLO is only slightly lower than model 4 [19] in precision by 1.4%, and slightly lower than model 2 and model 4 in map 50:95 by 1.2% and 1.3%, respectively. Meanwhile, in order to verify whether the embedding position of ODConv convolution is optimal or not, ODConv is used to replace the conventional convolution of neck network (model 2).…”

Section: Od-yolo Comparison Experimentmentioning

confidence: 88%

ODCS-YOLO detection algorithm for rail surface defects based on omni-dimensional dynamic convolution and context augmentation module

Gao,

Gu,

Yin

et al. 2024

Meas. Sci. Technol.

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To solve the problems of easy miss and false detection on rail surface defects caused by small size, dense target, and high similarity between features and background, this paper proposed an improved detection algorithm in complex background. First, the conventional convolution of YOLOv5 backbone network is replaced with omni-dimensional dynamic convolution (ODConv), which improves the feature extraction capability of the network without increasing the computational cost; second, to improve the model’s performance in detecting tiny objects, a two-layer context augmentation module (CAM) is introduced into the path aggregation network (PAN) structure; finally, the traditional non-maximum suppression (NMS) algorithm is replaced by the Soft-NMS algorithm in the network post-processing to reduce the false-alarm and miss-rate. The experimental results on the Railway Track Fault Detection public dataset show that the OD-YOLO (OD stands for ODConv) and C-PAN (CAM module is introduced into PAN) structures could achieve better performance in the same type of improved algorithms; compared with the baseline algorithm YOLOv5, the ODCS-YOLO (OD stands for ODConv, C stands for CAM and S stands for Soft-NMS) algorithm improves the precision by 12.4%, the recall by 3.6%, the map50 by 8.6% and the GFLOPs is reduced by 0.6. Compared with seven classical object detection algorithms, the ODCS-YOLO algorithm achieves the highest detection accuracy, which makes it able to meet the real-time detection requirements of rail surface defects in real working conditions. The ODCS-YOLO model provides certain technical support for the defects detection and a new method for the detection of dense small objects.

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Section: Od-yolo Comparison Experimentmentioning

confidence: 88%

ODCS-YOLO detection algorithm for rail surface defects based on omni-dimensional dynamic convolution and context augmentation module

Gao,

Gu,

Yin

et al. 2024

Meas. Sci. Technol.

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“…Using different types of convolutions will give the model different features. When processing the features, we used three convolutions, DSConv [52], and GNConv [53] for validation. The comparison results are shown in Table 4:…”

Section: Ablation Experimentsmentioning

confidence: 99%

Intelligent detection method of microparticle virus in silkworm based on YOLOv8 improved algorithm

Zhang,

Su,

Wang

et al. 2024

J Supercomput

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The presence of microparticle viruses significantly impacts the quality of silkworm seeds for domestic sericulture, making their exclusion from detection in silkworm seed production crucial. Traditional methods for detecting microparticle viruses in silkworms, such as manual microscopic observation, molecular biology, and immunological approaches, are cumbersome and unable to achieve intelligent, batch real-time detection. To address this challenge, we employ the YOLOv8 algorithm in this paper. Firstly, NAM attention is introduced in the original algorithm’s Backbone component, allowing the model to extract more generic feature information. Secondly, ODConv replaces Conv in the Head component of the original algorithm, enhancing the model’s ability to identify microparticle viruses. Finally, NWD-LOSS modifies the CIoU loss of the original algorithm to obtain a more accurate prediction box. Experimental results demonstrate that the NN-YOLOv8 model outperforms mainstream detection algorithms in detecting silkworm microparticle diseases. With an average detection time of 22.6 milliseconds per image, the model shows promising prospects for future applications. This model improvement enhances detection efficiency and reduces human resource costs, effectively realizing detection intelligence.

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DSConv: Efficient Convolution Operator

Cited by 2 publications

References 23 publications

ODCS-YOLO detection algorithm for rail surface defects based on omni-dimensional dynamic convolution and context augmentation module

ODCS-YOLO detection algorithm for rail surface defects based on omni-dimensional dynamic convolution and context augmentation module

Intelligent detection method of microparticle virus in silkworm based on YOLOv8 improved algorithm

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