Rajiv Gandhi college of engineering Research and Technology , Chandrapur
Guided by : Kapesh Raghatate
Mr. Ketan khokale
Mr. Tushar Panghate
Mr. Sagar Mishra
Mr. Arbaz shaikh
The project entitled “ OBJECT DETECTION” is done to detect all instances of objects from known class such as people , car, buildings etc. Efficient and accurate object detection has been an important topic in the advancement of computer vision systems. With the advent of Machine learning techniques, the accuracy for object detection has increased drastically. The project aims to detect incorporate state-of-the-art technique for object detection with the goal of achieving high accuracy with a real-time performance. In the Object detection we use Tensor flow it is a free and open source software library for data flow differential programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications.
In this Object detection application we can detect multiple objects on the same time that is the advantage of this application and to make this application we use android studio which is specifically designed for android development.
Many problems in computer vision were saturating on their accuracy before a decade. However, with the rise of Machine learning techniques using tensor flow, the accuracy of these problems drastically improved. One of the major problem was that of image classification, which is defined as predicting the class of the image. A slightly complicated problem is that of image localization, where the image contains a single object and the system should predict the class of the location of the object in the image (a bounding box around the object.
The more complicated problem (this project), of object detection involves both classification and localization. In this case, the input to the system will be a image, and the output will be a bounding box corresponding to all the objects in the image, along with the class of object in each box.
II. Literature Review
Object detection is the identification of an object in the image along with its localisation and classification. It has wide spread applications and is a critical component for vision based software systems. This paper seeks to perform a rigorous survey of modern object detection algorithms that use deep learning. As part of the survey, the topics explored include various algorithms, quality metrics, speed/size trade offs and training methodologies. This paper focuses on the two types of object detection algorithms- the SSD class of single step detectors and the Faster R-CNN class of two step detectors. Techniques to construct detectors that are portable and fast on low powered devices are also addressed by exploring new lightweight convolutional base architectures. Ultimately, a rigorous review of the strengths and weaknesses of each detector leads us to the present state of the art.
III. Discussion and Conclusion
An accurate and eﬃcient object detection system has been developed which achieves comparable metrics with the existing state-of-the-art system. This project uses recent techniques in the ﬁeld of computer vision and machine learning. This can be used in real-time applications which require object detection for pre-processing in their pipeline. Addition of a temporally consistent network would enable smooth detection and more optimal than per-frame detection.
IV . References
- https://en.wikipedia.org/wiki/flightcance ilation and delay
- https://www.kaggle.com/tylerx/flights-and-airports-data for Dataset
- Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
- Ross Girshick. Fast R-CNN. In International Conference on