YOGA: Deep object detection in the wild with lightweight feature learning and multiscale attention
Abstract: Highlights•YOGA is a new object detection model that learns richer representation via attention based multi-scale feature fusion with a much lighter model that reduces nearly half convolution filters.•We provide a theoretical explanation of how label smoothing facilitates backpropagation during training, by mathematically analyzing how the loss gradient vector is involved in the recursive backpropagation algorithm. We also overcome overfitting using Genetic Algorithm based hyper-parameter tuning.•We compare YOGA with over 10 state-of-the-art deep learning object detectors and demonstrate the superiority of YOGA on the joint performance of model size and accuracy.•We migrate YOGA to real hardware (Jetson Nano 2GB) to assess its usability in the wild. Our experiments show that YOGA is well suited for even the lowest-end deep learning edge devices.
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