### SSD Backbones  
MobileNets are a tangled class of neural networks developed by Google researchers in 2017 [17]. These models are considered very useful for implementation on mobile and embedded devices. MobileNet uses depthwise separable convolutions. It significantly reduces the number of parameters compared to a network with regular convolutions with the same depth in the nets [17]. The main difference between normal convolutions and depth-wise convolutions is that in regular convolutions, the convolution operation is applied to all input channels, while in depth-wise convolutions, each channel remains separate [17] (see Figure 4). In MobileNet-V1, after applying a filter to each channel, a \(1\times 1\) convolutional layer is applied to combine the results of the depthwise convolution operation. Because depth-separable convolution requires less computation than regular convolution, MobileNets are faster and consume less power, so they can run on mobile and embedded devices without powerful graphics processors.  
Figure 3: \(8\times 8\) Feature Map with 4 default boxes at position \((3,3)\). For each of the boxes, four offsets and class values are predicted for \(p\) classes.  
Figure 2: SSD architecture.  
Due to the small size, there is a trade-off in accuracy compared to larger, fully convolutional architectures. MobileNet-V2 [18] was designed to improve the accuracy of the MobileNet-V1. In MobileNet-V2, there are two types of blocks. One is a residual block with a stride of one and the other is a block with a stride of two for reduction (see Figure 5). Adding residual blocks allows the network to access previous feature maps that were not changed in the convolutional block. Additionally, a linear layer was used in the last layer of the residual block to prevent nonlinearities from destroying too much information. The other difference with MobileNet-V1 is that a low-cost \(1\times 1\) convolutional layer was used at the beginning of the blocks to reduce the number of input channels. This way, the following \(3\times 3\) convolutional layer has much less parameters.  
MobileNet Edge TPU [28] was developed for the Edge TPU in the Pixel 4, which is similar in architecture to the Edge TPU in the Coral product, but adapted to the requirements of Pixel 4's main camera functions. To optimize the model accuracy and latency of the Edge TPU, the Neural Architecture Search (NAS) [29] and NetAdapt [30] algorithms  
Figure 4: (**A**) shows a normal convolution with \(8\times 8\times 1\) output and (**B**) shows a depthwise convolution with three kernels to get an image with \(8\times 8\times 3\) image.  
Figure 5: Blocks used in MobileNet-V2.  
were used to search for the model structure. NAS is a process that searches among all possible combinations of submodules that can be repeatedly assembled to obtain the entire model with the best possible accuracy. The NetAdapt algorithm works with the number of filters in each convolutional layer. To find the best structure for MobileNet Edge TPU in terms of accuracy and latency, the NAS and NetAdapt algorithms were used side by side. The result is a structure that has lower latency or higher accuracy at fixed latency than existing mobile models such as MobileNet-V2 and the minimalistic MobileNet-V3 [28].  
MobileDet [20] is a new architecture for image classification models designed specifically for use on mobile accelerators such as DSP, Edge TPU, and so on. In MobileNet Edge TPU, the NAS algorithm was used to find the best architecture for the classification model, but in [20], a new backbone search space is proposed based on full convolutions for the SSD object detection model. Figure 6 shows the three blocks used to build the MobileDet model.