Summary of Scale-invariant Object Detection by Adaptive Convolution with Unified Global-local Context, By Amrita Singh et al.
Scale-Invariant Object Detection by Adaptive Convolution with Unified Global-Local Context
by Amrita Singh, Snehasis Mukherjee
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel object detection model is introduced, addressing limitations in current Convolutional Neural Network (CNN) architectures for detecting small objects in images. The problem lies in the pooling process, which can result in the loss of dense features essential for minute object detection. Atrous convolution has been proposed to mitigate this issue, but it may compromise multi-scale detection capabilities. To overcome these limitations, the authors propose a Switchable Atrous Convolutional Network (SAC-Net) based on the efficientDet model. The SAC-Net incorporates a switchable mechanism that dynamically adjusts atrous rates during forward passes, allowing for improved performance in multi-scale object detection tasks while preserving dense features. Additionally, depth-wise switchable atrous rates and global context are applied to further enhance scale-invariant feature extraction. Experimental results on benchmark datasets demonstrate the proposed model’s superiority over state-of-the-art models in terms of accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to detect small objects in images is presented. Current methods often fail to find tiny objects because they lose important details during processing. Atrous convolution helps, but it can also make it harder for the model to detect objects at different scales. To fix this, researchers propose a new type of network that can adjust how it looks at an image while still keeping the good features. This new network is called SAC-Net and is based on another successful approach called efficientDet. The SAC-Net has two special features: it can change how it looks at an image during processing and it applies these changes in a way that helps detect objects of different sizes. The researchers tested their method on several datasets and found that it outperformed other state-of-the-art methods. |
Keywords
» Artificial intelligence » Cnn » Convolutional network » Feature extraction » Neural network » Object detection
