The non-coal electronic seal system uses yolov7+python network model technology, and the non-coal electronic seal system uses intelligent AI video analysis to monitor and analyze the changes in the number of personnel entering and exiting the mine in real time, the production and operation status of non-coal and coal mines, and automatically generate and push alarms information, prompting relevant personnel to take emergency measures. Python is a general-purpose programming language developed by Guido van Rossum, which quickly became very popular mainly because of its simplicity and code readability. It enables programmers to express ideas in fewer lines of code without loss of readability. Python is slow compared to languages like C/C++. That said, Python can be easily extended using C/C++, which allows us to write computationally intensive code in C/C++ and create Python wrappers that can be used as Python modules. This gives us two benefits: first, the code is as fast as raw C/C++ code (since it's actual C++ code working behind the scenes), and second, it's easier to write code in Python than in C/C++. OpenCV-Python is a Python wrapper around the original OpenCV C++ implementation.
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 new E-ELAN does not change the gradient transmission path of the original architecture at all, which uses group convolution to increase the cardinality of the added features, and combines the features of different groups in the way of shuffle and merge cardinality. This mode of operation can enhance the features learned from different feature maps, and improve the use of parameters and computational efficiency. It reaches a steady state regardless of the gradient path length and the stacked number of computational blocks in large-scale ELAN. If more computing blocks are infinitely stacked, this stable state may be broken and the parameter utilization rate will decrease. The newly proposed E-ELAN uses expand, shuffle, and merge cardinality to continuously enhance the learning ability of the network without destroying the original gradient path.
In terms of architecture, E-ELAN only changes the architecture of the computing block, while the architecture of the transition layer remains unchanged at all. The strategy of YOLOv7 is to use group convolutions to expand the channels and cardinality of computational blocks. The researchers will apply the same group parameters and channel multipliers to all computation blocks of the computation layer. Then, the feature maps calculated by each calculation block are shuffled into g groups according to the set group parameter g, and then they are connected together. At this point, the number of channels for each set of feature maps will be the same as in the original architecture. Finally, the method adds g sets of feature maps to perform merge cardinality. In addition to maintaining the original ELAN design architecture, E-ELAN can also guide different groups of computing blocks to learn more diverse features. Therefore, for series-based models, we cannot analyze the different expansion factors separately, but must consider them together. The research proposes Figure (c), that is, when extending the cascade-based model, only the depth in the calculation block needs to be expanded, and the remaining transmission layers are expanded correspondingly. This composite extension approach preserves the identity and optimal structure of the model at the time of its initial design.
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.