Infrared Ship Target Detection Method Based on Deep Convolution Neural Network

Wenxiu, 王文秀 Wang; Yutian, 傅雨田 Fu; Feng, 董峰 Dong; Feng, 李锋 Li

doi:10.3788/aos201838.0712006

Cited by 12 publications

(4 citation statements)

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“…而纹理分析法过于依赖人工设计的特征, 以致方法的适应性不强, 应用于不同场景时表现不佳 [15] . 目前, 深度学习被广泛应用于图像分类 [16] 、目标检测 [17] (cross entropy, CE) [25] , 二分类条件下的 CE 表示为 Sigmoid 函数 [26] 常被用作神经网络中的激活函数, 用于将变量映射至 0~1, 表示为 [27] 与交并比(intersection over union, IoU) [4] (1 ) ( ) [19] (记作 FCN), 文献 [6]方法(记作 en-de), 文献 [7]中的方法(记作 u-net), deeplabv3+方法 [28] (1) 在图 9 易辨场景对比中, en-de 和 u-net 的…”

Section: 目前已有大量针对烟尘图像分割问题的研unclassified

Industrial Smoke Target Segmentation Based on Fully Convolutional Networks with Multiscale Convolution and Dynamic Weight Loss Function

Liu¹,

Zhang²,

Li³

2020

J Comput-Aided Design Comput Graphics

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Smoke blackness is an important indicator in industrial pollution monitoring. Aiming at how to use computer image recognition technology to effectively segment the target area of smoke from the background in the automatic monitoring of Ringelmann scale, and the characteristics of industrial smoke and dust with unfixed shapes and high cloud similarity, leading to the inaccurate results of existing methods to segment smoke in complex scenes. An improved FCN model was proposed, combined multi-scale convolution operations to enhance the feature extraction ability of the network. Dynamic weights were added to the basis of cross entropy to enhance the training of inaccurate points, and the accuracy of segmentation was further improved. The experimental results with the actual factory smoke emission image data set shows that the proposed model is more accurate than the other models in the complex scene, the F 1 -score and the IoU metrics are significantly improved.

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Section: 目前已有大量针对烟尘图像分割问题的研unclassified

Industrial Smoke Target Segmentation Based on Fully Convolutional Networks with Multiscale Convolution and Dynamic Weight Loss Function

Liu¹,

Zhang²,

Li³

2020

J Comput-Aided Design Comput Graphics

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“…Secondly, small targets occupy little pixels and lack obvious texture and structure characteristics [4]. Thirdly, it is very difficult for the algorithm to balance the arithmetic speed and the detection effect [5,6]. Hence, infrared small and dim target detection is very difficult.…”

Section: Introductionmentioning

confidence: 99%

Research on Infrared Dim and Small Target Detection Algorithm Based on Low-Rank Tensor Recovery

Liu

Wang²

2023

J. of Syst. Eng. Electron.

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In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection, an improved detection algorithm of infrared small and dim target is proposed in this paper. Firstly, the original infrared images are changed into a new infrared patch tensor mode through data reconstruction. Then, the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics, and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness. Finally, the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image, and the final small target is worked out by a simple partitioning algorithm. The test results in various spacebased downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate. It is a kind of infrared small and dim target detection method with good performance.

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“…This approach is faster than the two-stage algorithms. Based on deep learning techniques, numerous researchers have made improvements to the respective algorithms in their specific research fields to detect ship targets at sea [11][12][13][14]. These advancements have yielded better detection results compared to comparative algorithms.…”

Section: Introductionmentioning

confidence: 99%

Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features

Lu,

Tan,

Wang

et al. 2023

Applied Sciences

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In infrared images of the sea surface, apart from the complex background of the sea surface, there are often sky and island backgrounds. The disturbances caused by sea wind and the reflection of intense sunlight on the sea surface increase the complexity of the background, which seriously hinders the detection of targets. To achieve the detection of dark-polarity ship targets in such environments, a dual-channel threshold segmentation method based on local low-gray region detection and geometric features judgment is proposed in this paper. In one channel, adaptive threshold segmentation is performed on the low-gray regions of the acquired image and combined with geometric features to obtain a finer segmentation result. In the other channel, adaptive segmentation is performed on the preprocessed image, and potential backgrounds that may be finely segmented as targets are filtered out based on an area threshold. Finally, the results of the two channels are multiplied and fused to obtain an accurate segmentation result. Experimental results demonstrate that the proposed algorithm outperforms the comparison algorithm in subjective and objective evaluations. The proposed algorithm in this paper not only achieves a low false alarm rate but also exhibits a higher detection rate, and the average detection rate in the test sequence surpasses 95%.

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Infrared Ship Target Detection Method Based on Deep Convolution Neural Network

Cited by 12 publications

References 0 publications

Industrial Smoke Target Segmentation Based on Fully Convolutional Networks with Multiscale Convolution and Dynamic Weight Loss Function

Industrial Smoke Target Segmentation Based on Fully Convolutional Networks with Multiscale Convolution and Dynamic Weight Loss Function

Research on Infrared Dim and Small Target Detection Algorithm Based on Low-Rank Tensor Recovery

Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features

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