# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 3 Key Considerations of Object Recognition on Edge Computing
### Lightweight Deep Learning Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Key Features** & **Advantages** & **Limitations** & **Common Use Cases** \\ \hline MobileNet [64] & Designed for mobile and edge devices, focusing on efficiency. & Low computational cost; supports quantization & \begin{tabular}{c} Slightly lower accuracy compared to larger models for complex tasks \\ \end{tabular} & \begin{tabular}{c} Real-time image classification; \\ object detection \\ \end{tabular} \\ \hline TinyYOLO [69] & Simplified YOLO model with fewer layers and parameters. & High inference speed; supports smaller devices & Accuracy trade-off for speed & \begin{tabular}{c} Real-time \\ surveillance; \\ autonomous driving \\ \end{tabular} \\ \hline EfficientNetLite [73] & Optimized version of EfficientNet for edge devices with compound scaling. & Excellent trade-off between accuracy and efficiency & Requires specific tuning for edge deployment & \begin{tabular}{c} Image classification; \\ object detection \\ demanding high \\ accuracy \\ \end{tabular} \\ \hline NASNetMobile [74] & \begin{tabular}{c} AutoML-designed \\ lightweight architecture for mobile devices. \\ \end{tabular} & High accuracy; optimized structure & \begin{tabular}{c} More \\ resource-intensive \\ than MobileNet \\ \end{tabular} & \begin{tabular}{c} Mobile classification \\ and detection tasks \\ \end{tabular} \\ \hline SqueezeNet [75] & \begin{tabular}{c} Compact architecture \\ using \(1\times 1\) convolutions to reduce parameters. \\ \end{tabular} & Very small model size; low latency. & Lower accuracy than modern architectures & \
\begin{tabular}{c} Resource- \\ constrained \\ applications; \\ resource constraint \\ hardware like FPGA \\ \end{tabular} \\ \hline ShuffleNet [76] & Uses pointwise group convolution and channel shuffle for efficiency. & High speed; low computational cost &
\begin{tabular}{c} Slightly lower accuracy compared to \\ MobileNet, may \\ require specialized \\ hardware for \\ optimal performance \\ \end{tabular} & Embedded systems, AR/VR applications \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Lightweight deep learning model. object recognition models on edge AI devices, where resources like memory, storage, and processing power are limited [77]. There are five well-known model compression techniques, as listed in Table 5. Model quantization reduces parameter precision from floating-point (e.g., floating points (FPs) 32 bits) to lower-bit formats (e.g., integer (INT) 8 bits), significantly shrinking memory usage and accelerating inference on constrained edge devices [78]. On the other hand, model pruning and sparsity techniques focus on removing redundant weights, channels, or layers to compress the network structure, thus reducing storage and computational resource usage [79]. Knowledge distillation transfers knowledge from a larger, complex model (the teacher) to a smaller, more efficient model (the student), allowing the student model to achieve similar performance while using significantly fewer parameters [80]. Additionally, low-rank factorization breaks large-weight matrices into products of smaller matrices, lowering the total parameter count and decreasing the required number of multiply-accumulate operations [81]. Finally, layer fusion can combine multiple layers into one to reduce computational overheads and improve efficiency [82]. Model optimization techniques can be applied individually or in combination to achieve the best balance between accuracy and resource consumption for specific edge devices.