FOMO, An efficient way to do Object detection on Edge devices

BANGALORE, India - Feb. 21, 2023 - PRLog -- n today's world, we are witnessing a proliferation of AI solutions. However, in many cases, these solutions fail to reach consumers due to the high hardware resource requirements needed to run these models. To scale our AI journey, we require solutions that are efficient, faster, and accurate enough to run on edge devices. This is where FOMO comes into the picture.

Object detection is a crucial aspect of computer vision that has been explored for many years. Deep learning and neural networks have revolutionized the field, enabling more precise and accurate results in object detection. Popular deep learning-based algorithms and model architectures like R-CNNs and their variants are prevalent in object detection. However, feature-based methods like Haar Cascades, SIFT, SURF, and HOG still play a significant role in certain applications. The strengths and weaknesses of these methods should be considered when selecting the best approach.

Object detection techniques have greatly benefited from Convolutional Neural Networks, but their usage requires specialized hardware and computational resources. tinyML has enabled deep learning on microcontrollers, making real-time multi-object detection possible on constrained devices. This breakthrough has brought about new possibilities for object detection applications, as deep learning models can now be run directly on the devices that detect them.

TinyML has made great strides in image classification, which predicts the presence of an object in an image. However, object detection requires identifying multiple objects and their bounding boxes, making it more complex and memory-intensive. Traditional object detection models processed images multiple times, but newer models like YOLO use single-shot detection for near real-time results. However, these models still require large memory and data sets, making it challenging to run them on small devices and detect small objects.

FOMO (Faster Objects, More Objects) is a concept that challenges the idea that all object-detection applications require high-precision output from deep learning models. It suggests that by balancing accuracy, speed, and memory, deep-learning models can be reduced to small sizes while remaining useful. One way this can be achieved is by predicting the object's centre rather than detecting bounding boxes. Many object detection applications only require the location of objects in the frame, not their sizes, and detecting centroids is more compute-efficient than bounding box prediction while requiring less data.

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