After insertion, the device initializes and then operates normally. Depending upon the operating system the host may also initialize resulting in a hot swap. The powered host or device are not necessarily in a quiescent state.
The problem with YOLO is that it leaves much accuracy to be desired. SSDs, originally developed by Google, are a balance between the two. The algorithm is more straightforward and I would argue better explained in the original seminal paper than Faster R-CNNs. We can also enjoy a much faster FPS throughput than Girshick et al.
To learn more about SSDs, please refer to Liu et al.
Efficient deep neural networks Figure 2: Left Standard convolutional layer with batch normalization and ReLU. Right Depthwise separable convolution with depthwise and pointwise layers followed by batch normalization and ReLU figure and caption from Liu et al.
When building object detection networks we normally use an existing network architecture, such as VGG or ResNet, and then use it inside the object detection pipeline.
The problem is that these network architectures can be very large in the order of MB. Network architectures such as these are unsuitable for resource constrained devices due to their sheer size and resulting number of computations.
Instead, we can use MobileNets Howard et al. MobileNets differ from traditional CNNs through the usage of depthwise separable convolution Figure 2 above.
The general idea behind depthwise separable convolution is to split convolution into two stages: This allows us to actually reduce the number of parameters in our network.
The problem is that we sacrifice accuracy — MobileNets are normally not as accurate as their larger big brothers… …but they are much more resource efficient.
For more details on MobileNets please see Howard et al. Combining MobileNets and Single Shot Detectors for fast, efficient deep-learning based object detection If we combine both the MobileNet architecture and the Single Shot Detector SSD framework, we arrive at a fast, efficient deep learning-based method to object detection.Object detection with deep learning and OpenCV.
In the first part of today’s post on object detection using deep learning we’ll discuss Single Shot Detectors and MobileNets.. When combined together these methods can be used for super fast, real-time object detection on resource constrained devices (including the Raspberry Pi, smartphones, etc.).
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research.
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