Object detection technology has matured to a point at which exciting applications are becoming possible. Indeed, industry has created a variety of computer vision products and services from the traditional area of machine inspection to more recent applications such as video surveillance, or face recognition. In this chapter, several representatives from industry present their views on the use of computer vision in industry. Current research conducted in industry is summarized and prospects for future applications and developments in industry are discussed. The project is designed to demonstrate detection of any object ahead of the ultrasonic transducer. It can be used for applications like security area monitoring and wild life photography.

 

CHAPTER ONE
1.1                                                        INTRODUCTION
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Every object class has its own special features that helps in classifying the class – for example all circles are round. Object class detection uses these special features. For example, when looking for circles, objects that are at a particular distance from a point (i.e. the center) are sought. Similarly, when looking for squares, objects that are perpendicular at corners and have equal side lengths are needed. A similar approach is used for face identification where eyes, nose, and lips can be found and features like skin color and distance between eyes can be found.
With the increasing demand for autonomous projects, the use of sensors is increasing. Sensors are sophisticated devices that convert the physical parameter into a signal which can be measured electrically.
They are very important to robotics. It is able to give robots remote access and make decisions as for a desired environment.
It can also perceive its own environment and through programming can get the output it desires. Out of these, this detector is based on the Ultrasonic technology. [1]
In industrial applications, ultrasonic sensors are characterized by their reliability and outstanding versatility. Ultrasonic sensors can be used to solve even the most complex tasks involving object detection or level measurement with millimeter precision, because their measuring method works reliably under almost all conditions.

1.2                                             OBJECTIVE OF THE PROJECT
The main objective of this work is to design a device that detects an object that will be ahead of ultrasonic transducer, if the object is permanently or intermittently within the sensor’s maximum range. With this device object of a certain class such as humans, buildings, or cars can be detected.
At the end of this work, students involved will be able to understand ultrasonic and buzzer and go deeper into learning microcontroller.

1.3                                         SIGNIFICANCE OF THE PROJECT
It is used in face detection and face recognition. It is also used in tracking objects, for example tracking a ball during a football match, tracking movement of a cricket bat, tracking a person in a video.

1.4                                                 SCOPE OF THE PROJECT
The project is designed to demonstrate detection of any object ahead of the ultrasonic transducer. This ultrasonic detector consists of two parts: an emitter, which produces a 40 kHz sound wave; and a detector, which detects this 40 kHz sound wave and sends the signal back to the microcontroller.
The ultrasonic module is interfaced to the microcontroller of 8051 family. Whenever any object approaches near the ultrasonic module, the signal transmitted the transmitter is reflected by this object and is received by the module. When the microcontroller receives the signal from ultrasonic receiver it actuates the output to take the appropriate action.
An LCD is interfaced to the microcontroller for displaying the status of the module like whether the object is detected or not.

1.5                                          APPLICATION OF THE PROJECT
Face detection
Popular applications include face detection and people counting.

People Counting
Object detection can be also used for people counting, it is used for analyzing store performance or crowd statistics during festivals. These tend to be more difficult as people move out of the frame quickly (also because people are non rigid objects).

Vehicle detection
Similarly when the object is a vehicle such as a bicycle or car, object detection with tracking can prove effective in estimating the speed of the object. The type of ship entering a port can be determined by object detection (depending on shape, size etc). This system for detecting ships is currently in development in some European countries.
Manufacturing Industry
Object detection is also used in industrial processes to identify products. Say you want your machine to only detect circular objects.  Hough circle detection transform can be used for detection.
Online images
Apart from these object detection can be used for classifying images found online. Obscene images are usually filtered out using object detection.
Security
In the future we might be able to use object detection to identify anomalies in a scene such as bombs or explosives (by making use of a quadcopter).

1.6                                  FUTURE TECHNOLOGY OF THE PROJECT
Further the project can be enhanced by interfacing a GSM modem to the project so that, when the object is detected then the controller sends an SMS to the user.

1.7                                        PROJECT WORK ORGANISATION
The various stages involved in the development of this project have been properly put into five chapters to enhance comprehensive and concise reading. In this project thesis, the project is organized sequentially as follows:
Chapter one of this work is on the introduction to this study. In this chapter, the background, significance, objective application and scope of this study were discussed.
Chapter two is on literature review of this study. In this chapter, all the literature pertaining to this work was reviewed.
Chapter three is on design methodology. In this chapter all the method involved during the design and construction were discussed.
Chapter four is on testing analysis. All testing that result accurate functionality was analyzed.
Chapter five is on conclusion, recommendation and references.

References

1. The NIST humanid evaluation framework (2003), http://www.frvt.org
2. The TREC video retrieval evaluation (2003), http://www.nlpir.nist.gov/projects/trecvid
3. The Pascal visual object classes challenge (2005), http://www.pascalnetwork.org/challenges/VOC
4. Chan, M., Hoogs, A., Schmiederer, J., Petersen, M.: Detecting rare events in video using semantic primitives with HMM. In: Proc. ICPR, vol. 4, pp. 150–154 (2004)Google Scholar
5. Chan, M., Hoogs, A., Perera, A., Bhotika, R., Schmiederer, J., Doretto, G.: Joint recognition of complex events and track matching. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2006)Google Scholar
6. Fergus, R., Zisserman, A., Perona, P.: Object class recognition by unsupervised scale-invariant learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2003) (2003)Google Scholar
7. Garcia, C., Delakis, M.: Convolutional face finder: A neural architecture for fast and robust face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1408–1423 (2004)CrossRefGoogle Scholar
8.  "Object Class Detection". Vision.eecs.ucf.edu. Retrieved 2013-10-09.
9.  "ETHZ - Computer Vision Lab: Publications". Vision.ee.ethz.ch. Retrieved 2013-10-09.

 

 

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