Unmanned Surface Vessel Visual Object Detection Under All-Weather Conditions with Optimized Feature Fusion Network in YOLOv4

Sun, Xiaoqiang; Liu, Tao; Xin, Yu; Pang, Bo

doi:10.1007/s10846-021-01499-8

Cited by 27 publications

(7 citation statements)

References 33 publications

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“…The results show that their algorithm can achieve automatic analysis and statistical data extraction in waterways videos. Sun et al (2021) optimized the backbone network CSPDarkNet of YOLOv4 for application in an auxiliary intelligent ship navigation system. They added a receptive field block module, and the FPN of YOLOv4 was improved by combining the Transformer mechanism.…”

Section: Ship Detection Based On Yolomentioning

confidence: 99%

Lightweight object detection algorithm based on YOLOv5 for unmanned surface vehicles

Zhang

Jin

et al. 2023

Front. Mar. Sci.

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Visual detection technology is essential for an unmanned surface vehicle (USV) to perceive the surrounding environment; it can determine the spatial position and category of the object, which provides important environmental information for path planning and collision prevention of the USV. During a close-in reconnaissance mission, it is necessary for a USV to swiftly navigate in a complex maritime environment. Therefore, an object detection algorithm used in USVs should have high detection s peed and accuracy. In this paper, a YOLOv5 lightweight object detection algorithm using a Ghost module and Transformer is proposed for USVs. Firstly, in the backbone network, the original convolution operation in YOLOv5 is upgraded by convolution stacking with depth-wise convolution in the Ghost module. Secondly, to exalt feature extraction without deepening the network depth, we propose integrating the Transformer at the end of the backbone network and Feature Pyramid Network structure in the YOLOv5, which can improve the ability of feature expression. Lastly, the proposed algorithm and six other deep learning algorithms were tested on ship datasets. The results show that the average accuracy of the proposed algorithm is higher than that of the other six algorithms. In particular, in comparison with the original YOLOv5 model, the model size of the proposed algorithm is reduced to 12.24 M, the frames per second reached 138, the detection accuracy was improved by 1.3%, and the mean of average precision (0.5) reached 96.6% (from 95.3%). In the verification experiment, the proposed algorithm was tested on the ship video collected by the “JiuHang 750” USV under different marine environments. The test results show that the proposed algorithm has a significantly improved detection accuracy compared with other lightweight detection algorithms.

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Section: Ship Detection Based On Yolomentioning

confidence: 99%

Lightweight object detection algorithm based on YOLOv5 for unmanned surface vehicles

Zhang

Jin

et al. 2023

Front. Mar. Sci.

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“…Next, variants of Resnet-50, VGG16, ResNext50, or Darknet52, can be the backbone, which refers to the network that takes the image as input and retrieves the feature map. The neck and head are backbone sub-sets that maximize the discriminability and robustness of the function using FPN, PAN, RFB, etc., and the forecast-managing head [23][24][25]. For a single-stage detector such as YOLO and SSD, this may either be used for dense prediction or FRCNN and Mask RCNN, a twostage detector also known as Sparse Prediction.…”

Section: Yolov4 For Detectionmentioning

confidence: 99%

Human Face Emotions Recognition from Thermal Images Using DenseNet

Prasad

Chandana

2023

Int. j. electr. comput. eng. syst. (Online)

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In the current scenario face identification and recognition is an important technique in surveillance. The face is a necessary biometric in humans. Therefore face detection plays a major job in computer vision applications. Several face recognition and emotions classification approaches have been presented throughout the last few decades of research to improve the rate of face recognition for thermal pictures. However, in real-time, lighting conditions might change due to several factors, such as the different times of capture, weather, etc. Due to variations in lighting intensity, the performance of the facial expression recognition system is not good. This paper proposed a model for human thermal face detection and expression classification. Four main steps were involved in this research. Initially, the Difference of the Gaussian (DOG) filter is utilized to crop the input thermal images and then normalize the images using the median filter in pre-processing step. Then, Efficient Net is used for extracting features such as shape, location, and occurrences from thermal face images. After that, detect human faces utilized by the YOLOv4 technique to better emotions classification. Finally, classify the emotions on faces by using the DenseNet technique into seven emotions such as happy, sad, disgust, surprise, anger, fear, and neutral. The proposed method outperforms state-of-art techniques for face recognition on thermal pictures, and classifies the expressions, according to experimentations on the RGB-D-T database. The accuracy, precision, recall, and f1-score metrics will be utilized with the database to assess the efficacy of the proposed methodology. The proposed models achieve a high classification accuracy of 95.97% on the RGB-D-T database. Furthermore, the outcomes show good precision for various face recognition tasks.

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“…Take the 13 × 13 grid as an example, which is equal to dividing the input Mel spectrogram into 13 × 13 squares; then, each square will be preset with three prior frames. The classification results of the network will adjust the positions of these three prior boxes and finally filter by the nonmaximum suppression (NMS) algorithm [ 28 ], so as to get the final classification results.…”

Section: Spp-yolo-v4 Network Structurementioning

confidence: 99%

Research on Audio Recognition Based on the Deep Neural Network in Music Teaching

Cui

2022

Computational Intelligence and Neuroscience

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Solfeggio is an important basic course for music majors, and audio recognition training is one of the important links. With the improvement of computer performance, audio recognition has been widely used in smart wearable devices. In recent years, the development of deep learning has accelerated the research process of audio recognition. However, there is a lot of sound interference in music teaching environment, which leads to the performance of the audio classifier that cannot meet the actual demand. In order to solve this problem, an improved audio recognition system based on YOLO-v4 is proposed, which mainly improves the network structure. First, Mel frequency cepstrum number is used to process the original audio and extract the corresponding features. Then, try to apply the YOLO-v4 model in the field of deep learning to the field of audio recognition and improve it by combining with the spatial pyramid pool module to strengthen the generalization ability of data in different audio formats. Second, the stacking method in ensemble learning is used to fuse the independent submodels of two different channels. Experimental results show that compared with other deep learning technologies, the improved YOLO-v4 model can improve the performance of audio recognition, and it has better performance in processing data of different audio formats, which shows better generalization ability.

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Unmanned Surface Vessel Visual Object Detection Under All-Weather Conditions with Optimized Feature Fusion Network in YOLOv4

Cited by 27 publications

References 33 publications

Lightweight object detection algorithm based on YOLOv5 for unmanned surface vehicles

Lightweight object detection algorithm based on YOLOv5 for unmanned surface vehicles

Human Face Emotions Recognition from Thermal Images Using DenseNet

Research on Audio Recognition Based on the Deep Neural Network in Music Teaching

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