padmini588 / yolov3

Yolov3 Implementation in Keras

SECTION 1 : PROJECT TITLE

Bird Recognition based on the YOLO Algoritm

---

SECTION 2 : EXECUTIVE SUMMARY / PAPER ABSTRACT

In this project we present YOLO v3, a new approach to object detection. YOLO can be expanded as You Only Look Once. The prior work on object detection was carried out as a two-step process. They first generate a potential bounding box in an image and then use a classifier on those boxes. After classification, post-processing is used to refine the bounding boxes, eliminate duplicate detections and rescore the boxes based on the other objects in the scene. These methods are complex and are time-consuming. Also, they are hard to train and optimize because each individual component must be trained separately. YOLO eliminates these problems by carrying out the whole detection process in a single pipeline. As its name suggests, uses a single neural network to predict bounding boxes and class probabilities directly from full images in one evaluation . This project describes the procedure of bird detection and classification using YOLO v3 , inferences from training.

---

SECTION 3 : CREDITS / PROJECT CONTRIBUTION

Official Full Name	Student ID (MTech Applicable)	Email (Optional)
SHASHANK NIGAM	A0198469A	[email protected]
RAMDAS KRISHNAKUMAR	A0198518M	[email protected]
PADMINI RAMESH	A0198465L	[email protected]

---

SECTION 4 : PROJECT REPORT / PAPER

<Github File Link> : https://github.com/Shashankwer/Yolov3/blob/master/Project_report.docx

---