In this paper, we propose a garbage classification model that integrates the attention mechanism and multiple network optimization methods. First, we construct a four-category primary network for recyclable garbage, kitchen garbage, hazardous garbage, and other garbage. And then, four secondary networks are constructed to map the above 4 primary categories to 40 secondary classes. Both the primary and secondary networks take Resnet101 as the main backbone network and integrate attention mechanism, Focal loss function, and warm-up learning rate. The experimental results prove that the proposed model has a high classification performance for the HUAWEI cloud garbage classification dataset.
In this paper, a new vehicle counting and traffic flow monitoring system is designed based on deep learning and image recognition. For accurate recognition of vehicles, Mask R-CNN model has been adopted and improved. Vehicle dataset is set up to obtain the corresponding model weight as recognition backbone in software. In addition, two counting methods, regional counting method and tracking counting method, have been analyzed and combined for effective counting. The experimental results show that the recognition rate of the proposed system is almost 100% and the counting rate is about 93.5%. According to the counting results, the planning requirement of vehicle path optimization is realized.
Traditional hyperspectral image classification typically uses raw spectral signatures without considering the spatial characteristics. In this paper, we proposed a novel method for hyperspectral image classification based on morphological attribute profiles. We employed independent component analysis for dimensionality reduction and designed an extended multiple attribute profiles (EMAP) to extract spatial features in ICA-induced subspaces. For accurate classification, we proposed a Bayesian maximum a posteriori formulation that couples EMAPs-based feature extraction for the class-conditional probability with an MRF-based prior. Experimental results show that the proposed method substantially outperforms traditional and state-of-the-art methods tending to result in smoother classification maps with fewer erroneous outliers.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.