Pig Detection Algorithm Based on Sliding Windows and PCA Convolution

Sun, Lei; Liu, Yan; Chen, Shuaihua; Luo, Bing; Li, Yiyang; Liu, Chunhong

doi:10.1109/access.2019.2907748

Cited by 10 publications

(12 citation statements)

References 25 publications

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“…Except for recall, the HOG-based system also exceeds the ConvNet-based systems in sizes of the performance measure values. Further, for this [6,6] px [8,8] px [10,10] px [12,12] px [14,14] px [16,16] setting, the HOG-based system has significantly lower time complexity when compared to the ConvNet-based systems (Figure 12). Considering all these facts, we conclude that the HOG-based passenger recognition system, with polynomial kernel function of degree 3 and cells of size [10,10] px, best fits the requirements for implementation into the low-cost automated real-time passenger counting system.…”

Section: Discussionmentioning

confidence: 97%

“…A set of candidate object images generated by a search algorithm from a frame is imbalanced (often highly) [12,13] with a predominance of images without complete heads (Figure 5). As conventional SVMs are not suitable for the imbalanced learning tasks [47], the training and testing sets must be balanced to get unbiased results.…”

Section: Training and Testing Setsmentioning

confidence: 99%

“…e regions determine candidate object images, which are classified using an appropriate object recognition system. e proposition of regions can be carried out using an exhaustive method such as a sliding window [12] or using an advanced time-efficient method such as a selective search algorithm [13]. In modern object detection systems, both the location and the recognition are carried out by a single deep neural network [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Person Detection for an Orthogonally Placed Monocular Camera

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Němec

et al. 2020

Journal of Advanced Transportation

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Counting of passengers entering and exiting means of transport is one of the basic functionalities of passenger flow monitoring systems. Exact numbers of passengers are important in areas such as public transport surveillance, passenger flow prediction, transport planning, and transport vehicle load monitoring. To allow mass utilization of passenger flow monitoring systems, their cost must be low. As the overall price is mainly given by prices of the used sensor and processing unit, we propose the utilization of a visible spectrum camera and data processing algorithms of low time complexity to ensure a low price of the final product. To guarantee the anonymity of passengers, we suggest orthogonal scanning of a scene. As the precision of the counting is relevantly influenced by the precision of passenger recognition, we focus on the development of an appropriate recognition method. We present two opposite approaches which can be used for the passenger recognition in means of transport with and without entrance steps, or with split level flooring. The first approach is the utilization of an appropriate convolutional neural network (ConvNet), which is currently the prevailing approach in computer vision. The second approach is the utilization of histograms of oriented gradients (HOG) features in combination with a support vector machine classifier. This approach is a representative of classical methods. We study both approaches in terms of practical applications, where real-time processing of data is one of the basic assumptions. Specifically, we examine classification performance and time complexity of the approaches for various topologies and settings, respectively. For this purpose, we form and make publicly available a large-scale, class-balanced dataset of labelled RGB images. We demonstrate that, compared to ConvNets, the HOG-based passenger recognition is more suitable for practical applications. For an appropriate setting, it defeats the ConvNets in terms of time complexity while keeping excellent classification performance. To allow verification of theoretical findings, we construct an engineering prototype of the system.

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Section: Discussionmentioning

confidence: 97%

Section: Training and Testing Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Person Detection for an Orthogonally Placed Monocular Camera

Škrabánek

Doležel

Němec

et al. 2020

Journal of Advanced Transportation

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“…However, this method has a limitation that it takes a long time to operate on the embedded board. [81] suggests that the performance of the classification problem is improved by applying PCA dimension reduction. Therefore, we will consider a quick detection method that combines our proposed model with PCA as an interesting future research topic.…”

Section: Dimensionality Reduction and Texturementioning

confidence: 99%

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

et al. 2020

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Automated pig monitoring is an important issue in the surveillance environment of a pig farm. For a large-scale pig farm in particular, practical issues such as monitoring cost should be considered but such consideration based on low-cost embedded boards has not yet been reported. Since low-cost embedded boards have more limited computing power than typical PCs and have tradeoffs between execution speed and accuracy, achieving fast and accurate detection of individual pigs for “on-device” pig monitoring applications is very challenging. Therefore, in this paper, we propose a method for the fast detection of individual pigs by reducing the computational workload of 3 × 3 convolution in widely-used, deep learning-based object detectors. Then, in order to recover the accuracy of the “light-weight” deep learning-based object detector, we generate a three-channel composite image as its input image, through “simple” image preprocessing techniques. Our experimental results on an NVIDIA Jetson Nano embedded board show that the proposed method can improve the integrated performance of both execution speed and accuracy of widely-used, deep learning-based object detectors, by a factor of up to 8.7.

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“…While many researchers have reported the detection of pigs using typical learning and image processing techniques, the detection accuracy for highly occluded images may not be at an acceptable level. Recently, end-to-end deep learning techniques have been proposed for object detection, and various pig-detection methods based on deep learning results (along with the typical learning and image processing techniques) have been reported [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. YOLOv4 [31] is a recently released detector that…”

Section: Introductionmentioning

confidence: 99%

EnsemblePigDet: Ensemble Deep Learning for Accurate Pig Detection

et al. 2021

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Automated pig monitoring is important for smart pig farms; thus, several deep-learning-based pig monitoring techniques have been proposed recently. In applying automated pig monitoring techniques to real pig farms, however, practical issues such as detecting pigs from overexposed regions, caused by strong sunlight through a window, should be considered. Another practical issue in applying deep-learning-based techniques to a specific pig monitoring application is the annotation cost for pig data. In this study, we propose a method for managing these two practical issues. Using annotated data obtained from training images without overexposed regions, we first generated augmented data to reduce the effect of overexposure. Then, we trained YOLOv4 with both the annotated and augmented data and combined the test results from two YOLOv4 models in a bounding box level to further improve the detection accuracy. We propose accuracy metrics for pig detection in a closed pig pen to evaluate the accuracy of the detection without box-level annotation. Our experimental results with 216,000 “unseen” test data from overexposed regions in the same pig pen show that the proposed ensemble method can significantly improve the detection accuracy of the baseline YOLOv4, from 79.93% to 94.33%, with additional execution time.

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Pig Detection Algorithm Based on Sliding Windows and PCA Convolution

Cited by 10 publications

References 25 publications

Person Detection for an Orthogonally Placed Monocular Camera

Person Detection for an Orthogonally Placed Monocular Camera

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

EnsemblePigDet: Ensemble Deep Learning for Accurate Pig Detection

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