object-detection

问题 Upon running the Android TF Detect Demo and using my retrained 3 class ssd_mobilenet_v1_coco model my TF detect Demo crashes giving an IndexOutOfBoundException which is 12-26 17:53:13.931 22429-25212/org.tensorflow.demo E/AndroidRuntime: FATAL EXCEPTION: inference Process: org.tensorflow.demo, PID: 22429 java.lang.ArrayIndexOutOfBoundsException: length=3; index=3 at java.util.Vector.arrayIndexOutOfBoundsException(Vector.java:907) at java.util.Vector.elementAt(Vector.java:328) at java.util

问题 I am training a model to extract all the necessary fields from a resume for which I am using mask rcnn to detect the fields in image. I have trained my mask RCNN model for 1000 training samples with 49 fields to extract. I am unable to improve the accuracy. How to improve the model? Is there any pretrained weights that may help? 回答1: Looks like you want to do text classification/processing, you need to extract details from the text but you are applying object detection algorithms. I believe

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 5 days ago . This is an algorithm question on the detectMultiScale(...) function from the opencv library. I need help to understand what opencv's detectMulitScale() function exactly does. I have understood from reading the C++ code that the source image is scaled with several scales based on scaleFactor and size

问题 I am searching for an object detection capability check which will identify IE9. Can you help me? 回答1: Check out this snippet by James Padolsey: // ---------------------------------------------------------- // A short snippet for detecting versions of IE in JavaScript // without resorting to user-agent sniffing // ---------------------------------------------------------- // If you're not in IE (or IE version is less than 5) then: // ie === undefined // If you're in IE (>=5) then you can

问题 I've started using OpenCV with a fair amount of success with a view to detecting different body parts (hands, arms, face, e.t.c). The first and most obvious approach to achieve this is using Haar or LBP cascade classifiers, but I would like to know what other options are out there. I've seen examples of skin detection using HSV images and detecting pixels within a color range. But this does not help determine which bit is which! I've also implemented an MOG2 background subtraction which can

问题 I wrote a program which gets matches between 2 pictures. And this is the code. but if I use BruteForceMatcher> (not flann) it works. #include <stdio.h> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" using namespace cv; void help() { printf("\nThis program demonstrates using features2d detector, descriptor extractor and simple matcher\n" "Using the SURF desriptor:\n" "\n" "Usage:\n matcher_simple <image1> <image2>\n"); } int

问题 I used tensorflow object detection api with RFCN_resnet101 for little objects, but sometimes the detection result is not good, it will detect the object with offset, and sometimes it detects an object by mistake. Does anyone knows how to deal with it? 回答1: Debugging object detection can be tricky. I recommend checking the input data (does it make sense): Do object bounding boxes get displayed correctly when overlayed with images? Are you using pixel box coordinates (vs normalized) when

问题 I am currently doing real time object detection of an orange ball with Raspberry Pi 3 Model B. The code below is supposed to take a frame, then with the cv2.inRange() function, filter out the image using RGB (BGR). Then I apply dialation and erosion to remove noise. Then I find the contours and draw them. This code worked until now. However when I ran it today without changing it, I got the folowing error: Traceback (most recent call last): File "/home/pi/Desktop/maincode.py", line 12, in

问题 I'm trying to use the Tensorflow Object Detection API and I've successfully tested the installation,but we I try to generate the PASCAL VOC TFRecord files with the given command python object_detection/create_pascal_tf_record.py \ --label_map_path=object_detection/data/pascal_label_map.pbtxt \ --data_dir=VOCdevkit --year=VOC2012 --set=train \ --output_path=pascal_train.record I encountered the following error: Traceback (most recent call last): File "object_detection/create_pascal_tf_record

问题 x = Conv2D(768, (3, 3), padding='same', activation='relu', kernel_initializer='normal', name='rpn_conv1',trainable=trainable)(base_layers) x_class = Conv2D(num_anchors, (1, 1), activation='sigmoid', kernel_initializer='uniform', name='rpn_out_class',trainable=trainable)(x) # stop gradient backflow through regression layer x_regr = Conv2D(num_anchors * 4, (1, 1), activation='linear', kernel_initializer='zero', name='rpn_out_regress',trainable=trainable)(x) How to use K.stop_gradient() to stop