# Seatbelt Detection Algorithm Improved with Lightweight Approach and Attention Mechanism
## 2 Related Studies
### Lightweight Technology  
Recently, CNNs have demonstrated extraordinary capabilities in various missions, including image recognition, target detection, voice synthesis, and semantic segmentation. However, competitive performance is chiefly achieved at the expense of deepening the convolutional layers, increasing the number of Floating-Point Operations (FLOPs) and model size. For instance, a standard VGG16 has more than 130 million parameters and requires more than 10 GFLOPs. How to deploy networks more efficiently and conveniently without substantially corrupting performance has become a current research hotspot.  
In recent years, a variety of approaches have been proposed to address the above-mentioned challenges, such as model pruning [23], neural architecture design [24], and model quantization [25].  
Model pruning is used to remove insignificant connections or channels within pre-trained neural networks in the light of a variety of evaluation standards to lower the network's volume and computing operations without decreasing algorithm performance [26]. Several strategies delete entire neurons or channels to better utilize hardware devices, such as Graphics Processing Units (GPUs). Ref. [27] proposed a Structured Sparsity Learning (SSL) approach to improve the construction of CNNs. The other technique primarily focuses on pruning single neuron parameters, generating a sparse and irregular network. Although the parameters are relatively small, such techniques can merely obtain an acceleration of inference under customized matrix libraries. Ref. [28] proposed a technique for eliminating redundant weights by leveraging the second derivative matrix, necessitating software libraries tailored to support the pruned network.  
Neural architecture design primarily centers around designing compact computing modules for CNNs to reduce network computation without compromising performance. The Xception [29] architecture employs separable convolution operation to improve the usage of model parameters efficiently. MobileNet [30], on the other hand, designs a lightweight deep neural module by splitting a vanilla convolution into a deepwise convolution and a pointwise convolution. This approach preserves the network's original feature representation capability while significantly lowering its computational complexity. ShuffleNet [31] addresses the problem of feature information interaction between channel groups by utilizing channel shuffle operations, enabling input and output channels to be correlated.  
Model quantization refers to representing weights or activations in CNNs with discrete values to lower memory overheads and achieve faster computation. The process of quantization transforms parameters from high-precision floating-point numbers to low-precision fixed-point numbers, such as 16 bit, 8 bit, and 4 bit. Binary and ternary techniques [32; 33] considerably accelerate the speed of models by quantifying real-valued weights into binary or ternary weights. Tensor decomposition methods [34] compress the model size or improve computation through utilizing redundancy and low-rank characteristics of the weights.