20191107 11350800 1.0 TRUE 20220915 110335 DetectObjectsUsingDeepLearning 20191107 In a raster analysis deployment, this tool runs a trained deep learning model on an input raster to produce a feature class containing the objects it identifies. The feature class can be shared as a hosted feature layer in your portal. The features can be bounding boxes or polygons around the objects found, or points at the centers of the objects. 005 D CNTK deep learning model detect objects raster analysis TensorFlow training ArcToolbox Tool 20191108 Environmental Systems Research Institute, Inc. (Esri) 380 New York Street Redlands California 92373-8100 info@esri.com United States 909-793-2853 909-793-5953 http://www.esri.com 007

In a raster analysis deployment, this tool runs a trained deep learning model on an input raster to produce a feature class containing the objects it identifies. The feature class can be shared as a hosted feature layer in your portal. The features can be bounding boxes or polygons around the objects found, or points at the centers of the objects.

DetectObjectsUsingDeepLearning_ra The input image used to detect objects. It can be an image service URL, a raster layer, an image service, a map server layer, or an internet tiled layer. The input image used to detect objects. It can be an image service URL, a raster layer, an image service, a map server layer, or an internet tiled layer. The function model arguments are defined in the Python raster function class referenced by the input model. This is where you list additional deep learning parameters and arguments for experiments and refinement, such as a confidence threshold for fine tuning the sensitivity. The names of the arguments are populated by the tool from reading the Python module on the RA server. The function model arguments are defined in the Python raster function class referenced by the input model. This is where you list additional deep learning parameters and arguments for experiments and refinement, such as a confidence threshold for fine tuning the sensitivity. The names of the arguments are populated by the tool from reading the Python module on the RA server. Specifies whether non maximum suppression, where duplicate objects are identified and the duplicate feature with a lower confidence value is removed, will be performed. NO_NMS—All detected objects will be in the output feature class. This is the default. NMS—Duplicate detected objects will be removed. Specifies whether non maximum suppression, where duplicate objects are identified and the duplicate feature with a lower confidence value is removed, will be performed. Unchecked—All detected objects will be in the output feature class. This is the default. Checked— Duplicate detected objects will be removed. The field in the feature service that contains the confidence scores that will be used as output by the object detection method. This parameter is required when the NMS keyword is used for the runNMS parameter. The field in the feature service that contains the confidence scores that will be used as output by the object detection method. This parameter is required when the Non Maximum Suppression parameter is checked. The name of the class value field in the feature service. If a field name is not specified, the tool will attempt to use a Classvalue or Value field. If these fields do not exist, the tool will identify all records as belonging to one class. The name of the class value field in the feature service. If a field name is not specified, the tool will attempt to use a Classvalue or Value field. If these fields do not exist, the tool will identify all records as belonging to one class. The maximum overlap ratio for two overlapping features, which is defined as the ratio of intersection area over union area. The default is 0. The maximum overlap ratio for two overlapping features, which is defined as the ratio of intersection area over union area. The default is 0. Your raster analysis (RA) server Python environment must be configured with the proper deep learning framework Python API such as Tensorflow, CNTK, or similar. With the tool running, your RA server calls a third-party deep learning Python API (such as TensorFlow or CNTK) and uses the specified Python raster function to process each raster tile. The Input Model parameter will only use a deep learning package (.dlpk) item from the portal. After the Input Model is selected or specified, the tool will obtain the model arguments information from your raster analysis server. The tool may fail to obtain such information if your input model is invalid or your raster analysis server isn’t properly configured with the deep learning framework. Use the Non Maximum Suppression parameter to identify and remove duplicate features from the object detection. For more information about deep learning, see Deep learning in ArcGIS Pro. This example creates a hosted feature layer in your portal based on object detection using the DetectObjectsUsingDeepLearning tool. import arcpy arcpy.DetectObjectsUsingDeepLearning_ra( "https://myserver/rest/services/Farm/ImageServer", "https://myportal/sharing/rest/content/items/itemId", "detectedTrees", "score_threshold 0.6;padding 0", "NO_NMS") This example creates a hosted feature layer in your portal based on object detection using the DetectObjectsUsingDeepLearning tool. #--------------------------------------------------------------------------- # Name: DetectObjectsUsingDeepLearning_example02.py # Requirements: ArcGIS Image Server # Import system modules import arcpy # Set local variables inImage = "https://myserver/rest/services/coconutFarmImage/ImageServer" inModel = "https://myportal/sharing/rest/content/items/itemId" outName = "detectedTrees" modelArgs = "score_threshold 0.6;padding 0" runNMS = "NMS" confScoreField = "Confidence" classVField = "Class" maxOverlapRatio = 0.15 # Execute Detect Objects Using raster analysis tool arcpy.DetectObjectsUsingDeepLearning_ra(inImage, inModel, outName, modelArgs, runNMS, confScoreField, ClassVField, maxOverlapRatio) ArcGIS geoprocessing tool that runs a trained deep learning model on an input raster to produce a feature class containing the objects it finds. withheld