# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 3 Key Considerations of Object Recognition on Edge Computing
### Lightweight Deep Learning Model  
Lightweight deep learning models, as shown by Table 4, are specifically designed for efficient object recognition on resource-constrained edge devices. MobileNet uses depthwise separable convolutions, which factorize a standard convolution into a depthwise convolution and a 1 \(\times\) 1 pointwise convolution to reduce computational complexity while maintaining significantly good accuracy, making it ideal for mobile and embedded systems [64]. MobileNet V2 [65] adds inverted residual blocks and linear bottlenecks, enhancing performance and memory efficiency while improving representational power for tasks like object detection and segmentation. MobileNet V3 [66] combined neural architecture search (NAS) and advances like SE (Squeeze-and-Excitation) blocks to optimize accuracy, latency, and energy consumption, providing both small (MobileNetV3-Small) and large (MobileNetV3-Large) variants for different use cases. The common characteristic across all versions of MobileNet is its focus on efficiency and scalability for mobile and edge devices, enabling high-performance deep learning with minimal computational and memory requirements. Tiny YOLO, a smaller variant of the YOLO architecture, prioritizes real-time detection with reduced model complexity [67]. There are some commonly used Tiny YOLO model versions. Tiny YOLO v3 is the reduced version of the YOLOv3. YOLOv3 was developed based on a robust Darknet-53 framework employing 53 convolution layers [68] to improve performance. YOLOv3-tiny introduced much-reduced network depth, seven convolution layers, and six max-pooling layers, significantly reducing the processing time [69]. Tiny YOLO v7 [70] is part of the YOLOv7 series [71]. The main idea of YOLOv7 is to introduce  
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trainable auxiliary architectures and optimize the network for real-time object detection. The modifications to create YOLOv7-tiny involved reducing the model size and using a simplified architecture. This significantly reduced the number of parameters to 6.2 million, compared to 36.9 million in YOLOv7. It also uses a leaky rectified linear unit (RELU), which has less complexity than the sigmoid linear unit (SILU) used in YOLOV7. Throughout their development, Tiny YOLO models have consistently aimed to balance speed, accuracy, and computational efficiency for surveillance, robotics, and IoT applications. There are also some well-known lightweight deep learning models other than MobileNet and Tiny YOLO. EfficientNet-Lite [72], a streamlined version of EfficientNet [73], employs compound scaling to balance model size, accuracy, and speed, offering excellent performance for classification and detection tasks on edge platforms. NASNet-Mobile [74], developed through neural architecture search (NAS) techniques, provides high accuracy with an optimized structure for mobile devices, though it is slightly more resource-intensive than other models. SqueezeNet and ShuffleNet focus on extreme compactness; SqueezeNet [75] achieves high efficiency through 1 \(\times\) 1 convolutions and parameter reduction, while ShuffleNet [76] leverages grouped convolutions and channel shuffling for fast and lightweight inference. These models are highly optimized for edge environments, enabling real-time and power-efficient object recognition in applications like IoT, autonomous systems, and AR/VR. Another technique to building a lightweight deep learning model is using model compression techniques. Model compression techniques are essential for deploying efficient