# Robot Bionic Vision Technologies: A Review
## 2 Literature Review
### Differences and Similarities between Human Vision and Bionic Vision  
The human visual system operates on a network of interconnected cortical cells and organic neurons. In contrast, a bionic vision system runs on an electronic chip composed of transistors, mainly through a camera with CMOS and CCD sensors, image acquisition, and sending the image to a special image-processing system to obtain the form of the recorded target information and encode it according to pixel, brightness, color, and other information. The image-processing system performs various operations on these codes to extract the characteristics of the target. Through specific equipment, it simulates the visual ability of human beings to make corresponding decisions or execute on these decisions [74]. Figure 4 depicts the human vision processing and representative machine vision processing processes. Human visual path: RGB light wave \(\rightarrow\) cornea \(\rightarrow\) pupil \(\rightarrow\) lens (light refraction) \(\rightarrow\) vitreous (supporting and fixing the eyeball) \(\rightarrow\) retina (forming object image) \(\rightarrow\) optic nerve (conducting visual information) \(\rightarrow\) lateral geniculate body \(\rightarrow\) optic chiasma \(\rightarrow\) primary visual cortex V1 \(\rightarrow\) other visual cortices V2, V3, V4, V5, etc. \(\rightarrow\) the brain understands and executes \(\rightarrow\) other nerves are innervated to perform corresponding actions. Bionic vision path: RGB color image \(\rightarrow\) image sensor (image coding) \(\rightarrow\) image processing (image decoding) \(\rightarrow\) image operator (image gray processing, image correction, image segmentation, image recognition, etc.) \(\rightarrow\) output results and execution. The information in the brain moves in multiple directions. Light signals move from the retina to the inferior temporal cortex, where they are transmitted to V1, V2, and other layers of the visual cortex. Simultaneously, each layer provides feedback on the previous layer. In each layer, neurons interact with each other and communicate information, and all interactions and connections essentially help the brain to fill in gaps in visual input and make inferences when information is incomplete. The machine mainly converts the image into a digital electrical signal through the image acquisition device and transmits it to the image processor. There are plenty of image processors, and the most common one is GPU. With professional AI computing requirements, ASIC (application-specific integrated circuit) emerged. ASIC performs better than CPU, GPU, and other chips, with higher processing speed and lower power consumption. However, ASIC is very expensive to produce [75; 76; 77]. For example, Google has developed an ASIC-based TPU that is dedicated to accelerating the computing power of deep neural networks [78]. In addition, there are also NPU, DPU, etc. [79; 80; 81]. FPGA is a processor for developers to customize; usually, an image processor integrates machine vision perception and recognition algorithms. There are diverse algorithm models, such as CNN, RNN, R-CNN, Fast R-CNN, Mask R-CNN, YOLO, and SSD [82; 83; 84], which vary in terms of performance and processing speed. The most classical and widely used algorithm is the convolutional neural network (CNN) algorithm. Specifically, the convolutional layer first extracts the initial features, then the pooling layer extracts the main features, and finally, the fully connected layer summarizes all features to provide a classifier for prediction and recognition. The convolutional and pooling layers are sufficient to identify the complex image contents, such as faces and car license plates. Data in artificial neural networks often move in a one-way manner. CNN is a \"feedforward network\", where the information moves step by step from the input layer to the next layer and the output layer. This network  
Figure 4: Human vision processing and Representative machine vision processing processes.