The construction site dust intelligent monitoring system uses the yolov7 network algorithm model technology to monitor the dust situation in the construction site in real time. The construction site dust intelligent monitoring system uses AI video intelligent analysis technology, and transmits the data to the data center for analysis. The development direction of YOLOv7 is different from the current mainstream real-time object detectors, and the research team hopes that it can support both mobile GPUs and GPU devices from the edge to the cloud. In addition to architecture optimization, the method proposed in this study also focuses on the optimization of the training process, focusing on some optimization modules and optimization methods. This may increase the training cost to improve the accuracy of object detection, but not the inference cost. The researchers refer to the proposed modules and optimization methods as trainable "bag-of-freebies".
The development direction of YOLOv7 is different from the current mainstream real-time object detectors, and the research team hopes that it can support both mobile GPUs and GPU devices from the edge to the cloud. In addition to architecture optimization, the method proposed in this study also focuses on the optimization of the training process, focusing on some optimization modules and optimization methods. This may increase the training cost to improve the accuracy of object detection, but not the inference cost. The researchers refer to the proposed modules and optimization methods as trainable "bag-of-freebies". Several trainable bag-of-freebies methods are designed, so that real-time target detection can greatly improve the detection accuracy without increasing the cost of reasoning; for the evolution of target detection methods, the researchers discovered two new problems: one is How the reparameterized module replaces the original module, and the second is how the dynamic label assignment strategy handles the problem of assigning to different output layers, and proposes a solution to these two problems;
Proposed "extend" and "compound scale" methods for real-time object detectors to efficiently utilize parameters and calculations; the method proposed in this study can effectively reduce the parameters of SOTA real-time object detectors by about 40% and 50% computational load, with faster inference speed and higher detection accuracy. In most of the literature on designing efficient architectures, the main considerations include the number of parameters, computational effort, and computational density. The design of CSPVoVNet in Figure 2(b) below is a variant of VoVNet. The architecture of CSPVoVNet analyzes the gradient path to enable the weights of different layers to learn more diverse features, making inference faster and more accurate. The ELAN in Figure 2 © considers the problem of "how to design an efficient network".
The Adapter interface defines the following methods:
public abstract void registerDataSetObserver (DataSetObserver observer)
Adapter represents a data source. This data source may change, such as adding data, deleting data, and modifying data. When the data changes, it must notify the corresponding AdapterView to make corresponding changes. In order to realize this function, the Adapter uses the observer mode. The Adapter itself is equivalent to the observed object, and the AdapterView is equivalent to the observer. Register the observer for the Adapter by calling the registerDataSetObserver method.
public abstract void unregisterDataSetObserver (DataSetObserver observer)
Unregister the observer by calling the unregisterDataSetObserver method.
public abstract int getCount () returns the number of data in the Adapter.
public abstract Object getItem (int position)
The data in the Adapter is similar to an array, and each item in it corresponds to a piece of data, and each piece of data has an index position, that is, position, and the corresponding data item in the Adapter can be obtained according to the position.
public abstract long getItemId (int position)
Get the id of the specified position data item, usually the position will be used as the id. In Adapter, relatively speaking, position is used more frequently than id.
public abstract boolean hasStableIds ()
hasStableIds indicates whether the id of the original data item will change when the data source changes. If it returns true, it means the Id remains unchanged, and if it returns false, it means it may change. The hasStableIds method of Adapter subclasses (including direct subclasses and indirect subclasses) provided by Android all return false.
public abstract View getView (int position, View convertView, ViewGroup parent)
getView is a very important method in Adapter, which will create corresponding UI items for AdapterView according to the index of the data item.