Sökning: "RetinaNet"

Visar resultat 1 - 5 av 13 uppsatser innehållade ordet RetinaNet.

  1. 1. A Comparison of Advanced DeepLearning Algorithms for Multi-digit Detection in Historical Documents

    Master-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Författare :Manaswini Chethireddy; Sai Teja Palla; [2023]
    Nyckelord :Deep Learning Methods; Handwritten digits; Digit Detection; Image processing on document images; YOLOV5; Faster R-CNN; RetinaNet; YOLOV7;

    Sammanfattning : Background: Historical handwritten documents are assets for future generations and should be appropriately secured, so handwritten digit detection plays an important role to preserve them. Handwritten digit detection is a fundamental problem and has been studied extensively for many years. LÄS MER

  3. 2. Pruning a Single-Shot Detector for Faster Inference : A Comparison of Two Pruning Approaches

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Karl Beckman; [2022]
    Nyckelord :Computer Vision; Object Detection; Single-Shot Detector; SSD-MobileNetV2; Iterative Pruning; Datorseende; Objektdetektering; Enstegsdetektor; SDD-MobileNet-V2; Iterativ Beskärning;

    Sammanfattning : Modern state-of-the-art object detection models are based on convolutional neural networks and can be divided into single-shot detectors and two-stage detectors. Two-stage detectors exhibit impressive detection performance but their complex pipelines make them slow. LÄS MER

  4. 3. CNN-Based Methods for Tree Species Detection in UAV Images

    Master-uppsats, Linköpings universitet/Datorseende

    Författare :Olle Sievers; [2022]
    Nyckelord :Machine Learning; CNN; UAV; Tree Species; Deep Learning; Tree Species Detection; Detection;

    Sammanfattning : Unmanned aerial vehicles (UAVs) with high-resolution cameras are common in today’s society. Industries, such as the forestry industry, use drones to get a fast overview of tree populations. LÄS MER

  5. 4. Scar detection using deep neural networks

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Christina Sunnegårdh; [2021]
    Nyckelord :;

    Sammanfattning : Object detection is a computer vision method that deals with the tasks of localizing and classifying objects within an image. The number of usages for the method is constantly growing, and this thesis investigates the unexplored area of using deep neural networks for scar detection. LÄS MER

  7. 5. Detecting illegal gold mining sites in the Amazon forest : Using Deep Learning to Classify Satellites Images

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Nathan Labbe; [2021]
    Nyckelord :Deep Learning; Image Classification; Image Segmentation; Environmental Monitoring; Illegal Gold Mines Detection; Djupinlärning; bildklassificering; bildsegmentering; miljöövervakning; upptäckt av illegala guldgruvor;

    Sammanfattning : Illegal gold mining in the Amazon forest has increased dramatically since 2005 with the rise in the price of gold. The use of chemicals such as mercury, which facilitate the gold extraction, increases the toxicity of the soil and can enter the food chain, leading to health problems for the inhabitants, and causes the environmental scourge we know today. LÄS MER