@inproceedings{29e92aa7d2754500a031acfe5b201425,
title = "CNN-based small object detection and visualization with feature activation mapping",
abstract = "Object detection is a well-studied topic, however detection of small objects still lacks attention. Detecting small objects has been difficult due to small sizes, occlusion and complex backgrounds. Small objects detection is important in a number of applications including detection of small insects. One application is spider detection and removal. Spiders are frequently found on grapes and broccolis sold at supermarkets and this poses a significant safety issue and generates negative publicity for the industry. In this paper, we present a fine-tuned VGG16 network for detection of small objects such as spiders. Furthermore, we introduce a simple technique called 'feature activation mapping' for object visualization from VGG16 feature maps. The testing accuracy of our network on tiny spiders with various backgrounds is 84%, as compared to 72% using fined-tuned Faster R-CNN and 95.32% using CAM. Even though our feature activation mapping technique has a mid-range of test accuracy, it provides more detailed shape and size of spiders than using CAM which is important for the application area. A data set for spider detection is made available online.",
keywords = "CNN, R-CNN, feature activation map, heat map, object detection",
author = "Medhani Menikdiwela and Chuong Nguyen and Hongdong Li and Marnie Shaw",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Image and Vision Computing New Zealand, IVCNZ 2017 ; Conference date: 04-12-2017 Through 06-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/IVCNZ.2017.8402455",
language = "English",
series = "International Conference Image and Vision Computing New Zealand",
publisher = "IEEE Computer Society",
pages = "1--5",
booktitle = "2017 International Conference on Image and Vision Computing New Zealand, IVCNZ 2017",
address = "United States",
}