Pig Detection Algorithm Based on Sliding Windows and PCA Convolution
An accurate and rapid pig detection algorithm based on video image processing technology can be helpful to identify abnormal pigs and take timely measures to reduce the incidence of diseases. In order to solve the problems of low computational efficiency and low precision in pig detection algorithm...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8675729/ |
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| author | Longqing Sun Yan Liu Shuaihua Chen Bing Luo Yiyang Li Chunhong Liu |
| author_facet | Longqing Sun Yan Liu Shuaihua Chen Bing Luo Yiyang Li Chunhong Liu |
| author_sort | Longqing Sun |
| collection | DOAJ |
| description | An accurate and rapid pig detection algorithm based on video image processing technology can be helpful to identify abnormal pigs and take timely measures to reduce the incidence of diseases. In order to solve the problems of low computational efficiency and low precision in pig detection algorithm based on sliding windows, this paper proposed a simple and efficient pig detection algorithm. A two-level support vector machine model was trained to calculate the probabilities of sliding windows by using gradient and gray distribution features of pigs. The principal component analysis convolution kernels were trained to extract foreground and background features of pig images. The support vector machine was used to classify sliding windows to obtain windows where pigs were located, and the non-maximum suppression algorithm was used to eliminate redundant windows to complete the target detection. The experiments showed that the proposed algorithm blending gradient and gray distribution features had a higher recall rate than the BING algorithm. The recall rate was up to 99.21% using 500 windows. The classification accuracy of sliding windows in this paper was 95.21%, which was higher than that of the PCANet. By calculating the omission detection rate, the misdetection rate, and the average detection time, it can be seen that in the detection methods of the proposed algorithm, BING + PCANet, faster rcnn and yolo, the performance of the proposed algorithm was optimal. |
| format | Article |
| id | doaj-art-7a1556e38e1c4ea385925a339e888fb7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7a1556e38e1c4ea385925a339e888fb72025-08-20T03:29:35ZengIEEEIEEE Access2169-35362019-01-017442294423810.1109/ACCESS.2019.29077488675729Pig Detection Algorithm Based on Sliding Windows and PCA ConvolutionLongqing Sun0https://orcid.org/0000-0002-3633-4067Yan Liu1Shuaihua Chen2Bing Luo3Yiyang Li4Chunhong Liu5College of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaSchool of Natural and Computational Sciences, Massey University, Auckland, New ZealandCollege of Information and Electrical Engineering, China Agricultural University, Beijing, ChinaAn accurate and rapid pig detection algorithm based on video image processing technology can be helpful to identify abnormal pigs and take timely measures to reduce the incidence of diseases. In order to solve the problems of low computational efficiency and low precision in pig detection algorithm based on sliding windows, this paper proposed a simple and efficient pig detection algorithm. A two-level support vector machine model was trained to calculate the probabilities of sliding windows by using gradient and gray distribution features of pigs. The principal component analysis convolution kernels were trained to extract foreground and background features of pig images. The support vector machine was used to classify sliding windows to obtain windows where pigs were located, and the non-maximum suppression algorithm was used to eliminate redundant windows to complete the target detection. The experiments showed that the proposed algorithm blending gradient and gray distribution features had a higher recall rate than the BING algorithm. The recall rate was up to 99.21% using 500 windows. The classification accuracy of sliding windows in this paper was 95.21%, which was higher than that of the PCANet. By calculating the omission detection rate, the misdetection rate, and the average detection time, it can be seen that in the detection methods of the proposed algorithm, BING + PCANet, faster rcnn and yolo, the performance of the proposed algorithm was optimal.https://ieeexplore.ieee.org/document/8675729/Target detectionpigprincipal component analysissliding window |
| spellingShingle | Longqing Sun Yan Liu Shuaihua Chen Bing Luo Yiyang Li Chunhong Liu Pig Detection Algorithm Based on Sliding Windows and PCA Convolution IEEE Access Target detection pig principal component analysis sliding window |
| title | Pig Detection Algorithm Based on Sliding Windows and PCA Convolution |
| title_full | Pig Detection Algorithm Based on Sliding Windows and PCA Convolution |
| title_fullStr | Pig Detection Algorithm Based on Sliding Windows and PCA Convolution |
| title_full_unstemmed | Pig Detection Algorithm Based on Sliding Windows and PCA Convolution |
| title_short | Pig Detection Algorithm Based on Sliding Windows and PCA Convolution |
| title_sort | pig detection algorithm based on sliding windows and pca convolution |
| topic | Target detection pig principal component analysis sliding window |
| url | https://ieeexplore.ieee.org/document/8675729/ |
| work_keys_str_mv | AT longqingsun pigdetectionalgorithmbasedonslidingwindowsandpcaconvolution AT yanliu pigdetectionalgorithmbasedonslidingwindowsandpcaconvolution AT shuaihuachen pigdetectionalgorithmbasedonslidingwindowsandpcaconvolution AT bingluo pigdetectionalgorithmbasedonslidingwindowsandpcaconvolution AT yiyangli pigdetectionalgorithmbasedonslidingwindowsandpcaconvolution AT chunhongliu pigdetectionalgorithmbasedonslidingwindowsandpcaconvolution |