Detection of pig based on improved RESNET model in natural scene

When using the deep convolution neural network for pig detection, whether in the training stage or the evaluating stage, a suitable data set is very important for extracting effective features. The image data used in this study were captured from a commercial pig breeding farm. While applying the model in a real pig breeding farm, several factors were considered in acquiring image data. First, various postures of pigs were captured in the experiment. Because of certain illumination that severely affects the detection accuracy, different illumination conditions were also considered in the work. Second, the model would be difficult to recognise pig objects under occlusion circumstances. Therefore, occlusion pig images were included in the data set. To expand the data set, partial Internet public data sets were also considered. A total of 3000 original images under different scenes were collected to create the training set. All the collected image data were processed using Python language. Some pig images in the image data set are shown in Figure 1.

Fig. 1

To detect the pig targets more effectively, RESNET is considered to complete the recognising task. RESNET replaces the larger convolution kernel with several small convolution kernels, which reduces the parameters of the model, increases the number of nonlinear activation functions and the computation cost is effectively controlled. However, the small convolution kernel stack increases the depth of the network, which makes model training difficult and increases the training time.

Different deep RESNET models differ in convolution layers including ResNet-18, ResNet-34, ResNet-50, ResNet-101 and ResNet-152. RESNET can be used to train the deep convolution neural network, and the network framework is relatively simple [9].

With the depth of the convolution neural network increases, the accuracy will rise to saturation, and then the accuracy will decrease. It is found that the error rate of the test set and training set is increasing, which demonstrates that this phenomenon is not due to overfitting. Because not only the error of the test set is increasing but also the error of the training set itself is increasing. To solve this problem, RESNET minimises the residual through fast connection to optimise the learning goal of the network [10]. RESNET's main idea is to add a direct connection channel in the network, that is, the highway network. The former network structure is a nonlinear transformation of the input, while the highway network allows to retain a certain proportion of the previous network layer in the output. RESNET's idea is very similar to that of the highway network, allowing the original input information to be directly transmitted to the later layer, as shown in Figure 2.

Fig. 2

The output of the network is shown in Figure 3 and it can be expressed as: (1) H(x)=y=F(X, {wi})+X H(x) = y = F\left( {X,\quad \left\{ {{w_i}} \right\}} \right) + X

Fig. 3

H(x) is an elementary mapping that represents network output that needs to be matched by multiple superposition layers. X indicates the input vector of the first layer and y is output. F(X,{wi}) represents the residual mapping that needs to be learned. F=ω_s s(ω₁X) indicates the activation function of ReLU [11]. In Eq. (1), the dimensions of the parameters (X and F) are required to be the same. When this condition is not satisfied, dimension matching can be performed by linear projection parameters, as shown in Eq. (2) (2) H(x)=y=w2s(w1X)+ωsX H(x) = y = {w_2}s\left( {{w_1}X} \right) + {\omega _s}X

ω_s is the same square matrix as in Eq. (1), and ω_s is only used for matching dimensions.

The structure design is very important for convolution neural networks. Usually, the convolution neural network with a more complex structure would have a stronger ability of feature expression. However, the training time is longer when using the RESNET model with more layers. It is necessary to make a trade-off between detection accuracy and training time. To solve this problem, this paper presents an improved RESNET model, which not only improves the detection accuracy but also reduces the training time of the whole network in pig detection [12]. The idea of improving the model is to reduce the number of network's layers, remove redundant connections and simplify the network's structure. Therefore, the training time of the network is reduced through these means. The ability of learning and feature extraction is improved by adding a convolution layer in the fast connection to form a new type of residual block and introducing new convolution cores, respectively.

2.3.1

Residual block design

There are two kinds of residual blocks in the improved RESNET structure in this paper. The first type keeps the original RESNET residual block unchanged. It is composed of two convolution layers. The output of the former layer serves as the convolution layer and the input of the fast connection. It is named class A residual block, and the structure is shown in Figure 3. The second type is composed of three convolution layers, which add the convolution layer at the fast connection. The output of the former layer serves as the input of the convolution layer and the fast connection [13]. The output is used as the input of the convolution layer at the fast connection, and the output of the former layer is connected to the next convolution layer. The output of this convolution layer and the output of the convolution layer on the fast connection line are used as the input of the next stage [14]. Here we call it Class B residual block, and the structure is shown in Figure 4.

Fig. 4

In Figures 3 and 4, Conv1 and Conv2 represent the first convolution layer and the second convolution layer, respectively. BN is a batch normalisation (BN) algorithm. ReLU1 and ReLU2 are action function. When x>0, ReLU(x)=x, and when x<0, ReLU(x)=0.

2.3.2

Improve the overall structure of the model

Due to single residual block can not improve the classification accuracy, therefore, two types of residual blocks mentioned in Section 2.3.1 were combined to improve the CNN performance. Specifically, four A residual blocks and three B residual blocks were used in this work. To enhance the feature expression of the improved model, the number of convolution kernels in convolution layers is increased [15, 16]. The overall structure of the improved model is shown in Figure 5. In the improved model, the number of convolution cores in the convolution layer of conv1~conv9 remains unchanged. The number of convolution cores in conv10~conv17 is increased to 32. The size of convolution cores in the whole network structure is 3*3. ReLU was used as an activation function in the improved model.

Fig. 5

The improved RESNET model in this paper is compact with 32 convolution layers, and 3*3 convolution kernels are used in all convolution layers [17]. The original data set and the expanded data set are used for training the improved model, and the specific parameter settings in the training process are shown in Table 1.

In Table 1, test_iter indicates the times of forward pass in the test set, test_interval expresses that the test is executed every 500 times, base_lr shows the learning rate, weight_decay indicates the weight attenuation, gamma means the learning rate change index, max_iter is the maximum number of iterations [18], solver_mode indicates whether the training mode is GPU or CPU. GPU mode is used for training in this experiment. The improved RESNET model still applies the BN algorithm [19,20,21]. The idea of the BN algorithm is that the activation input value of a deep neural network gradually shifts or changes before making a nonlinear transformation. Therefore, the BN algorithm can transform the distribution of any neuron in each layer to the standard normal distribution with the mean value of 0 and the variance of 1. This means the activation input value would fall in the area where the nonlinear function is more sensitive to the input, therefore, the training speed can be greatly accelerated. The detection results applying the new improved model are shown in Figures 6 and 7.

Fig. 6

Fig. 7

From Figures 6 and 7, it can be seen that the improved RESNET model in this paper recognises a single pig with higher accuracy. The overall outline of the pig can be detected clearly; however, when detecting a pig in the group, some pigs may be missed due to occlusion.