Taiwan Centers for Disease Control (Taiwan CDC) is the competent authority responsible for the prevention and control of communicable diseases in Taiwan. Our mission is to protect people from the threats of communicable diseases.In recent years, with dramatic increases in international travel and the number of foreign laborers, various communicable diseases have been imported to Taiwan.
Facing the threat of emerging and re-emerging communicable diseases, Taiwan CDC has built up comprehensive surveillance network, such as National Notifiable Disease Surveillance System, and formulated policies for disease prevention, quarantine, and the capabilities of laboratory testing and research. The linkage among different systems provides real-time information exchange and leads to prompt response for possible outbreaks. Taiwan CDC devotes every effort to further strengthening research capacity and recruiting experts to combat the threats of communicable diseases in a scientific way.
Dengue fever is an acute infectious disease transmitted by mosquito. The peak time of dengue fever outbreak in Taiwan is usually at summertime. Mild clinical cases of dengue fever may present as symptoms such as fever, headaches, and myalgia while severe cases may have severe fluid leakage, hemorrhagic symptoms, shock, organ failure, coma and even death. The mortality rate can be as high as 20% or more if the patient does not receive proper treatment in time.
To effectively prevent dengue fever outbreak, cleaning up the breeding sites of the mosquitos is essential. Possible breeding sites for mosquitos include all containers that hold stagnant water, such as bottles, basins, buckets, cans, cups, bowls, tires, plastic bags, and etc.
Every year, the Taiwan Centers for Disease Control collaborate with local health department to examine the communities and to find uncleaned sites with those containers that may hold stagnant water, where may become mosquito breeding sites afterwards. However, the inspection takes tremendous manpower and time. This challenge provides labeled data for the various types of containers, and aims to build an object detection model for possible breeding sites. This way the inspectors can pinpoint the containers which hold stagnant water by digital camera images or live video, and thus improve the effectiveness of inspection and breeding site elimination.
This competition is a grand challenge of IEEE ICIP 2019. In addition to participating in the competition, participants can also submit the results of the solution(challenge paper) to the conference.
1st place: 3000 USD
2nd place: 2000 USD
3rd place: 1000 USD
All prizes are subject to the corresponding tax deductions, according to the law.
This event is conducted in National Standard Time (UTC+8). The schedule is as follows:
|2019/04/01||Registration opens, release the training datasets and Public test datasets. Public test dataset is open for testing. (Public Leaderboard)|
|2019/05/14||Release all the test datasets (including Public and Private datasets; Private Leaderboard will be announced when the topic ends)|
|2019/05/27||Submission of accompanying paper for possible publication at ICIP 2019 (optional)|
|2019/06/01 23:59:59||Solution results submission due date|
|2019/07/01||Notification of acceptance of accompanying papers (optional)|
|2019/07/15||Camera ready accepted papers submission (optional)|
|2019/09/22-25||Presentation of accepted contributions to the competition, results and winners awards at ICIP 2019|
This challenge hopes that distant objects (small objects) on the photo will be able to detect accurate. So, the metrics for small object detection, which means the area of its bounding box is less than 1024, use mean Average Precision (mAP) at intersection over union (IoU) threshold is 0.5.
For small objects, if the IoU of detection and ground truth is greater than 0.5, it is considered a True Positive, else it is considered a False Positive. Then we can get Precision.
The system evaluates its AP score for each small object, and then calculates the 13 type of water container object AP on average, and gets the mAP evaluation value. The participants will be ranked according to the criteria.
The system calculates mAP evaluation values using the COCO API
 Average Precision (AP): https://en.wikipedia.org/wiki/Evaluation_measures_%28information_retrieval%29#Average_precision
 intersection over union (IoU):
 COCO API: