Image data on vehicles was required to train a custom object detection model. Datasets on vehicles were obtained using the Stanford AI Lab – Cars Dataset which contained 16185 images of vehicles. This consisted of 196 different classes of cars with 8144 training images and 8041 testing images in jpg format. In addition, this dataset included bounding box information for each image describing the coordinates of the rectangle enclosing the vehicle. For model training, a desktop PC with an Intel i7-8700, NVIDIA RTX 2080, and 32 GB of RAM was used. To test inferencing on portable edge devices, a Raspberry Pi 4 with 4GB of RAM powered by a 4200 mAh battery pack was used. Additionally, the SSD_MobileNet_V2 model pretrained on the COCO dataset was imported from the Tensorflow Model Zoo before being trained on the custom vehicle data previously described. These existing weights were updated through the retraining process. Python was primarily used for software development in this project. The Numpy, Pandas, and Pillow libraries were employed to import, resize, and format the image data into the appropriate dimensions for the model architecture. The Anaconda package manager was used to install and manage python libraries while Jupyter Notebooks were used to write software and visualize data. The Cars Dataset from the Stanford AI lab was downloaded onto the project directory. The MATLAB file describing the 8144 training samples was loaded into a Pandas Dataframe using the python library mat4py. This dataframe consisted of 8 columns representing information about each image: the filename, width, height, class of vehicle in the image, and the four values representing the bounding box surrounding the vehicle in the image. Utilizing the bounding box coordinates, the vehicle was cropped out of the larger image and stored in a separate directory. Negative samp¬¬les, or background samples, were generated by a web crawler that collected 1000 images not containing a vehicle. These images tell the model what to avoid when detecting vehicles in each image. Additionally, training samples were generated by overlaying cropped vehicles on the obtained background samples. Positive samples were resized and cropped using the opencv-python library. Haar-Cascades are a machine-learning based approach in which positive samples and negative samples are used to train a model capable of detecting objects in an image. Positive images contain the target object which the model aims to detect, while negative images contain a variety of different objects or backgrounds which allows the model to learn to detect the target image under different conditions. Particularly, this model utilizes vertical and horizontal edge features to identify the parts of an image where pixel intensities dramatically change, marking the edges of objects. Figure 1 shows a visual representation of the matrices of 1s and 0s which make up these features. Upon training the model, an xml file containing the model was generated by the OpenCV library. Using this xml file, software was written to load the model into a python environment and perform bounding box predictions on vehicles in an image. The model was evaluated on a laptop running an Intel Core i7-9750h CPU at 2.6 GHz to determine the baseline performance. Then, the model was downloaded to a Raspberry Pi 4 to evaluate frames-per-second and latency metrics on real-time dashcam footage. A statistical test was required to determine the degree to which changing environments and the number of vehicles in an image affect the variation of latencies between the laptop and Raspberry Pi 4. A variety of tests were considered to eliminate the null hypothesis stating that the device used did not affect the variation in latencies, but ultimately the F-test was most suitable to analyze these results. A statistical F-test compares the variance of two datasets using a calculated F Statistic ((F-Test, n.d.). If the ratio of the two variances equals 1, it can be assumed that the null hypothesis is true.