Search

Abstract Aiming at the problems of error detection and missing detection in night target detection, this paper proposes a night target detection algorithm based on YOLOv7(You Only Look Once v7). The algorithm proposed in this paper preprocesses images by means of square equalization and Gamma transform. The GSConv(Group Separable Convolution) module is introduced to reduce the number of parameters and the amount of calculation to improve the detection effect. ShuffleNetv2_×1.5 is introduced as the feature extraction Network to reduce the number of Network parameters while maintaining high tracking accuracy. The hard-swish activation function is adopted to greatly reduce the delay cost. At last, Scylla Intersection over Union function is used instead of Efficient Intersection over Union function to optimize the loss function and improve the robustness. Experimental results demonstrate that the average detection accuracy of the proposed improved YOLOv7 model is 88.1%. It can effectively improve the detection accuracy and accuracy of night target detection.

Merchant ships sometimes fail to detect small ships at night and in poor visibility, leading to urgent situations and even collisions. Infrared (IR) cameras have inherent advantages in small target detection and become essential environmental awareness equipment on unmanned ships. The existing target detection models are complex and difficult to deploy on small devices. Lightweight detection algorithms are needed with the increase in the number of shipborne cameras. Therefore, herein, a lightweight model for small IR ship detection was selected as the research object. IR videos were collected in the Bohai Strait, the image sampling interval was calculated, and an IR dataset of small ships was constructed. Based on the analysis of the characteristics of the IR ship images, gamma transform was used to preprocess the images, which increased the gray difference between the target and background. The backbone of YOLOv5 was replaced with that of Mobilev3 to improve the computing efficiency. Finally, the results showed that the parameters of the proposed model were reduced by 83% compared with those of the YOLOv5m model, while the detection performance was almost the same. [ABSTRACT FROM AUTHOR]

Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment. [ABSTRACT FROM AUTHOR]

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, ground penetrating radar is the major technology for the detection of urban road collapses and disaster sources. Vehicle-mounted GPR collects tens of GB of data on site every day, but the present interpretation of abnormal regions detected by radar relies on manual interpretation with low process efficiency. The abnormal region images of GPR are different from the surrounding normal images. In terms of the features of abnormal regions in GPR images with an obvious brightness change and obvious directional characteristics, an abnormal region detection algorithm based on visual attention mechanism is proposed. Firstly, the complex background noise in the GPR images is suppressed by wavelet denoising and gamma transform, and the brightness and directional characteristics of the abnormal regions are enhanced. Secondly, by building a multi-scale image brightness and orientation feature pyramid model, the features of abnormal regions of interest are continuously enhanced, and the rapid screening of abnormal regions has been achieved. The effectiveness of the algorithm has been verified by actual tests on different types of abnormal radar image data. [ABSTRACT FROM AUTHOR]

Merchant ships sometimes fail to detect small ships at night and in poor visibility, leading to urgent situations and even collisions. Infrared (IR) cameras have inherent advantages in small target detection and become essential environmental awareness equipment on unmanned ships. The existing target detection models are complex and difficult to deploy on small devices. Lightweight detection algorithms are needed with the increase in the number of shipborne cameras. Therefore, herein, a lightweight model for small IR ship detection was selected as the research object. IR videos were collected in the Bohai Strait, the image sampling interval was calculated, and an IR dataset of small ships was constructed. Based on the analysis of the characteristics of the IR ship images, gamma transform was used to preprocess the images, which increased the gray difference between the target and background. The backbone of YOLOv5 was replaced with that of Mobilev3 to improve the computing efficiency. Finally, the results showed that the parameters of the proposed model were reduced by 83% compared with those of the YOLOv5m model, while the detection performance was almost the same.

Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment.

Extracting weak impulses from vibration signals is a tricky technique in fault diagnosis and condition monitoring of the rotating machinery. The autocorrelation function is an effective method for enhancing repetitive signals, however, the effect deteriorates as the point number of autocorrelation calculation decreases. To resolve this problem, a novel algorithm entitled extended autocorrelation function is presented. Besides, an algorithm called local maximum instantaneous extraction transform is proposed to further improve the enhancement effect, combining short-time Fourier transform, gamma transform, and local maximum sampling. Therefore, the presented algorithm is called the local maximum instantaneous extraction transform based on the extended autocorrelation function (LMIET-EACF). Firstly, we discuss the mathematical statistics theory of the autocorrelation function for realizing signal enhancement. It is illustrated that, when the number of data points involved in the calculation decreases, the amplitude variances of the noise components will increase. This is the limitation of the autocorrelation function. Secondly, an extension operation is conducted in the extended autocorrelation function to make the variance constant by keeping the number of calculation points constant. This operation can improve the enhancement effect for the fault impulses. Furtherly, the short-time Fourier transform is employed to suppress the noise. The contrast of the time–frequency distribution is improved by the gamma transform. And the local maximum sampling is employed to extract the fault impulse peaks to improve the time–frequency concentration. These operations constitute the local maximum instantaneous extraction transform. The proposed method is validated by a series of simulated signals and two public experimental signals. And a comparison is done between the LMIET-EACF and other mainstream signal enhancement algorithms. The results indicate that the proposed method performs better than several common time–frequency distributions, by quantitatively evaluating with the Rényi entropy. In addition, it can catch the fault signal in a lower signal-to-noise ratio, compared with some novel post-processing methods. [ABSTRACT FROM AUTHOR]

This paper presents a new and simple algorithm to eliminate haze from a single image using YIQ colour space and Dark Channel Prior (DCP). The suggested method consists of two parts. The first one is enhancement by adjusting contrast and lightness using contrast-limited adaptive histogram equalization and gamma transform. The second part involves applying DCP on a hazy image using the RGB colour model to achieve a haze-free image. The resulting image is then converted into YIQ, and the chrominance components (I and Q) are extracted and combined with the enhanced luminance component (Y) to achieve an enhancement haze-free image. The suggested method is applied to many outdoor images, and the quality of the resulting images is compared with that of images from other methods, by using several measures of quality. Analysing the results explain that the suggested method has a higher quality value than other methods. [ABSTRACT FROM AUTHOR]

High dynamic range (HDR) imaging has been developed for improved visual representation by capturing a wide range of luminance values. Owing to its properties, HDR content might lead to a larger privacy intrusion, requiring new methods for privacy protection. Previously, false colours were proved to be effective for assuring privacy protection for low dynamic range (LDR) images. In this work, the reliability of false colours when used for privacy protection of HDR images represented by tone‐mapping operators (TMOs) is studied. Two different TMO techniques are tested, a simple TMO based on the Gamma transform and a more complex local TMO. Moreover, two false colour palettes are also tested, and are applied to images that result from both TMOs and also to an LDR image that represents the centre exposure in the image sequence used to create the HDR image. The degree of privacy protection is analysed through both a subjective test using crowdsourcing and an objective test using face recognition algorithms. It is concluded that the application of the two studied false colour palettes reduces the recognition accuracy with respect to both tests. [ABSTRACT FROM AUTHOR]