Infrared maritime target detection based on edge dilation segmentation and multiscale local saliency of image details

Zhao, Enzhong; Dong, Lili; Dai, Hao

doi:10.1016/j.infrared.2023.104852

Cited by 2 publications

(2 citation statements)

References 47 publications

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“…The performance evaluation metrics of the model in this paper mainly use the mean pixel accuracy (mPA), mean intersection, and merger ratio (mIOU) to assess image segmentation model performance. Among them, mPA measures the average accuracy of the model in correctly predicting the pixels of each category-the higher the mPA value indicates, the better the pixel prediction accuracy of the model-which is calculated as shown in Equation (2). mIOU measures the segmentation accuracy of the model by calculating the ratio of the intersection and concatenation of the predicted segmentation results to the real segmentation results, with higher metrics indicating that the predicted results overlap with the real results and the model's segmentation effect is better.…”

Section: Indicators For Model Evaluationmentioning

confidence: 99%

“…In recent years, with the rapid development of deep learning, semantic segmentation [1], target detection [2], and image classification [3] have also made significant progress. Among them, in computer vision, semantic segmentation is a very important direction, and the main method used is to judge the category that this image belongs to by the pixels that have been labelled in the image.…”

Section: Introductionmentioning

confidence: 99%

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A Study of Kale Recognition Based on Semantic Segmentation

Wu,

Guo,

Liu

et al. 2024

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The kale crop is an important bulk vegetable, and automatic segmentation to recognize kale is fundamental for effective field management. However, complex backgrounds and texture-rich edge details make fine segmentation of kale difficult. To this end, we constructed a kale dataset in a real field scenario and proposed an UperNet semantic segmentation model with a Swin transformer as the backbone network and improved the model according to the growth characteristics of kale. Firstly, a channel attention module (CAM) is introduced into the Swin transformer module to improve the representation ability of the network and enhance the extraction of kale outer leaf and leaf bulb information; secondly, the extraction accuracy of kale target edges is improved in the decoding part by designing an attention refinement module (ARM); lastly, the uneven distribution of classes is solved by modifying the optimizer and loss function to solve the class distribution problem. The experimental results show that the improved model in this paper has excellent performance in feature extraction, and the average intersection and merger ratio (mIOU) of the improved kale segmentation can be up to 91.2%, and the average pixel accuracy (mPA) can be up to 95.2%, which is 2.1 percentage points and 4.7 percentage points higher than the original UperNet model, respectively, and it effectively improves the segmentation recognition of kale.

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Section: Indicators For Model Evaluationmentioning

confidence: 99%