## 3 The Proposed Model
### Feature Extraction Using SqueezeNet  
After pre-processing, the SqueezeNet model derives a collection of feature vectors from the pre-processed image. SqueezeNet is a lightweight and efficient CNN mechanism [19]. Using this model, we accomplish a 50\(\times\) reduction in module size in comparison with AlexNet, while exceeding or meeting the top-1 and -5 accurateness of AlexNet. The SqueezeNet module accomplishes remarkable outcomes with a small number of variables. There are two significant portions of CNN: classification and feature extraction. The extracted feature was employed for the precise image classification. Especially, these two portions of CNN accomplish the primary operation of CNN. The feature extraction of CNN encompasses convolution and sampling layers. The filtering of convolution layers is applied to diminish the noise in an image; later, the features of the image were enhanced. The convolution process was completed between the presentation layer convolutional kernel layer and the upper layer feature vector. Lastly, the activation function of CNN decides the computation of the convolution process. The effectiveness of training a NN was noticeable while applying the cost function (\(Z\)), which indicates the proportion between the reached output and trained instance [20].  
\[Z=\frac{-1}{m}\sum[x\;ln\;a(1-x)\ln(1-\beta)] \tag{4}\]  
In Equation (4), \(m\) characterizes the trainable dataset amount, \(x\) denotes the foreseeable value, and \(\beta\) indicates the original values in the resulting layer.  
The activation function role is a major portion of the classification technique with weighting the result of the CNN method and transmission kernel size. The ReLU activationfunction is in the middle of the commonly applied activation function. It is exploited from almost every CNN method for setting each negative value corresponding to zero. The zero setting inhibits several nodes contributed by the learning algorithm. An additional function, called ELU and LReLU, offers a slightly negative value and was hardly employed in the classification technique. ReLU activation function shows optimal results when compared to the LReLU activation function from the classification that is applied in this technique [21]. The ReLU activation function can be arithmetically formulated in the subsequent equation.  
\[ReLU(x)=max(0;\ x) \tag{5}\]  
This is more commonly applied in embedded settings; it involves distinct models of compression techniques. For example, \(3\times 3\) convolution kernels in the proposed technique are replaced by \(1\times 1\) convolution kernels. By using these techniques, the variable number for the individual convolution function is minimalized through a factor of 9. Furthermore, the \(3\times 3\) convolution kernel is reduced and down-sampling is hindered in the network. As a result, the suggested technique reduces the computation efforts and the number of trained variables. For that reason, it is practicable to establish SqueezeNet in memory constraint hardware devices. In contrast with the contemporary AI model, we perceived that SqueezeNet has a minimal variable amount; consequently, it is the better choice for robot vacuum applications. Inappropriately, the model size (viz., 6.1 MB) was higher in comparison with the memory space existing in the robot vacuum.