Waste classification based on deep neural networks is up against the dataset deficiency. However, that is too expensive and time-consuming for collecting and labeling waste samples. We proposed an improved ResNet-18 model based on Model Agnostic Meta-Learning (MAML) to improve classification accuracy with a few-shot waste classification dataset. the feature extraction part of the improved model includes a convolution layer and four residual blocks; the classification part of the improved model includes a max-pooling layer and three fully connected layers. Moreover, GroupNorm is adopted to reduce the impact of different feature distributions normalization on the classification accuracy. With initial parameters from the MAML training on the Mini-ImageNet dataset, the model improve accuracy only with one training iteration results on few waste samples. The experiments verified the effectiveness of our model on the Mini-ImageNet dataset and a few-shot waste classification dataset
In cities, a large amount of municipal solid waste has impacted on the ecological environment significantly. Automatic and robust waste detection and classification is a promising and challenging problem in urban solid waste disposal. The performance of the classical detection and classification method is degraded by some factors, such as various occlusion and scale differences. To enhance the detection model robustness to occlusion and small items, we proposed a robust waste detection method based on a cascade adversarial spatial dropout detection network(Cascade ASDDN). The hard examples with occlusion in pyramid feature space are generated and used to adversarial training a detection network. Hard samples are generated by the spatial dropout module with Gradient-weighted Class Activation Mapping. The experiment verifies the effectiveness of our method on the 2020 Haihua AI challenge waste classification.
Low light object detection is a challenging problem in the field of computer vision and multimedia. Most available object detection methods are not accurate enough in low light conditions. The main idea of low light object detection is to add an image enhancement preprocessing module before the detection network. However, the traditional image enhancement algorithms may cause color loss, and the recent deep learning methods tend to take up too many computing resources. These methods are not suitable for low light object detection. We propose an accurate low light object detection method based on pyramid networks. A low-resolution pyramid enhancing light network is adopted to lessen computing and memory consumption. A super-resolution network based on attention mechanism is designed before Efficientdet to improve the detection accuracy. Experiments on the10K RAW-RGB low light image dataset show the effectiveness of the proposed method.
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