Small Object Detection with lightweight PointNet Based on Attention Mechanisms

In response to the issue of subpar small object detection outcomes, the improved 3D object detection method for Point Net is proposed. First, multi-scale fusion method is added to the channel attention mechanism into the feature extraction stage of Point Net; then, the loss function is adjusted to optimize the backbone network and reduce the number of model parameters while improving the accuracy. The experimental results show that in the dataset, the proposed method has higher detection accuracy, the improved algorithm average accuracy improves 3.9%, the model size decreases 20%, effectively improves the detection effect of small objects, and realizes the lightweight of the network.