Description
Hello I want someone to help me with my assignment which is a writing paper . I will attach a pdf description of the assignments instruction so please make sure to follow the steps.
I choose option #1
Option 1 – Write a Short Literature Review and Design and Execute an ExperimentSelect at least two journal papers related to object detection in the context of construction (contentshould be as similar to excavator detection as you can find). Write a short literature review describingthe papers. Focus on the strengths and weaknesses of the works experimental design. What question were the authors trying to answer? How did the authors establish the importance of the question? Do you agree that their question was important? Why or why not? Did they design their experiment optimally to answer the question?Your literature review should be between 250 and 500 words.Next, design and execute your own experiment related to excavator detection in images. I haveprovided you with an annotated excavator dataset which is available on Canvas. I suggest you start withone of the experiment types listed below, but you are not strictly limited to these. Describe yourexperimental design in your final submission. Also document all steps and results of your process.Provide a discussion of results.
Experiment Type 1 Investigating the Performance Impact of a FeatureLook through the images provided to you in the Full Size Excavator Images folder. Identify afeature in the images you believe will impact the performance of the object detector eitherpositively or negatively. Look through the entire dataset and create a subset of the originaldataset by identifying all images where this feature is visible. Depending on the nature of thefeature, you will then test the features influence in either a binary or scale test set.Binary test set. Create two groups, one in which the feature is present, and a second in whichthe feature is absent. Make sure that there are no other systematic variations between the twogroups (i.e. control for confounding features). When you test your trained object detector onthe two groups, identify the impact of the feature on the ultimate performance of the network.Scale test set. Create a test set of images were your feature is visible. Characterize your featurein each image on a scale. Make sure that there are no other systematic variations along thescale (i.e. control for confounding features). When you test your trained object detector on thetest set, identify the impact of the feature on the ultimate performance of the network.
Experiment Type 2 Investigating the Performance Impact of Neural Network ArchitectureCompare the object detection performance of YOLOv2 with a different neural network type.Examples: YOLOv2 with a different backbone CNN, YOLOv3, SSD, and R-CNN. Draw someconclusions on the relationship between performance and some characteristics of the twonetworks types.https://www.mathworks.com/help/vision/examples.htm…
Experiment Type 3 Investigating the Performance Impact of Dataset SizeExample 1: Once additional data has been labelled by the class, incorporate this into the trainingdata and see how this improves performance on the original test set.Example 2: See if you can decrease the size of the training data by removing redundant imagesand see how the performance on the original test set changes.
