The safety belt wearing recognition system for height climbing operation uses the yolov5+python network framework model technology and the safety belt wearing recognition algorithm model for height climbing work to monitor whether the climbing work personnel are wearing safety belts and issue an alarm in time. The YOLO series algorithm is a typical one-stage target detection algorithm. It uses the anchor box to combine the regression problem of classification and target positioning, so as to achieve high efficiency, flexibility and good generalization performance, so it is also very popular in the industry. , and then we introduce the YOLO series of algorithms. Yolo means You Only Look Once, it does not really remove the candidate area, but creatively combines the candidate area and the target classification into one, and you can know which objects are there and where they are at a glance at the picture. The Yolo model uses the method of pre-defining the prediction area to complete the target detection. Specifically, the original image is divided into 7x7=49 grids (grid), and each grid allows the prediction of 2 bounding boxes (bounding box, containing a certain The rectangular box of the object), a total of 49x2=98 bounding boxes. We understand it as 98 prediction areas, which roughly cover the entire area of the picture, and target detection is performed in these 98 prediction areas.
Shortly after the appearance of YOLOV4, YOLOv5 was born. YOLOv5 has made further improvements on the basis of the YOLOv4 algorithm, and the detection performance has been further improved. Although the YOLOv5 algorithm has not been compared and analyzed with the YOLOv4 algorithm, the test effect of YOLOv5 on the COCO data set is quite good. Everyone is dubious about the innovativeness of the YOLOv5 algorithm, some people have a positive attitude towards it, and some people have a negative attitude towards it. In my opinion, there are still many places to learn in the YOLOv5 detection algorithm. Although these improvement ideas seem relatively simple or lack of innovation, they can definitely improve the performance of the detection algorithm.
YOLOv5 is a single-stage target detection algorithm. Based on YOLOv4, this algorithm has added some new improvement ideas, so that its speed and accuracy have been greatly improved. The main improvement ideas are as follows:
- Input : In the model training phase, some improvement ideas were proposed, mainly including Mosaic data enhancement, adaptive anchor frame calculation, and adaptive image scaling;
- Benchmark network : Integrating some new ideas in other detection algorithms, mainly including: Focus structure and CSP structure;
- Neck network : The target detection network often inserts some layers between the BackBone and the final Head output layer, and the FPN+PAN structure is added to Yolov5;
- Head output layer : The anchor frame mechanism of the output layer is the same as that of YOLOv4. The main improvement is the loss function GIOU_Loss during training and DIOU_nms for prediction frame screening.
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.