Abstract
The recent explosion of large volume of standard dataset of annotated images has offered promising opportunities for deep learning techniques in effective and efficient object detection applications. However, due to a huge difference of quality between these standardized dataset and practical raw data, it is still a critical problem on how to maximize utilization of deep learning techniques in practical agriculture applications. Here, we introduce a domain-specific benchmark dataset, called AgriPest, in tiny wild pest recognition and detection, providing the researchers and communities with a standard large-scale dataset of practically wild pest images and annotations, as well as evaluation procedures. During the past seven years, AgriPest captures 49.7K images of four crops containing 14 species of pests by our designed image collection equipment in the field environment. All of the images are manually annotated by agricultural experts with up to 264.7K bounding boxes of locating pests. This paper also offers a detailed analysis of AgriPest where the validation set is split into four types of scenes that are common in practical pest monitoring applications. We explore and evaluate the performance of state-of-the-art deep learning techniques over AgriPest. We believe that the scale, accuracy, and diversity of AgriPest can offer great opportunities to researchers in computer vision as well as pest monitoring applications.
Highlights
Object detection is a classic research topic in the computer vision communities
This leads us to the obvious question: how could we maximize the utilization of deep learning techniques in practical applications?
This paper offers a detailed analysis of AgriPest, where the validation set is split into four types of scenes that are common in practical pest monitoring applications
Summary
Object detection is a classic research topic in the computer vision communities. The current large volume of standardized object detection datasets [1,2,3] help to explore many key research challenges that are related to object detection and evaluate the performance of different algorithms and technologies. The recent popularity and development of deep learning techniques has proved a fact that, given sufficient high-quality annotated image datasets, deep learning approaches [4,5,6] can effectively and efficiently achieve the detection and classification tasks. This results in some practical breakthroughs in many classic applications, including face recognition [7] and vehicle detection [8]. In some domain-specific object detection applications, there is a huge difference of quality between standardized annotated dataset and practical raw data.
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