# Improving Moving Insect Detection with Difference of Features Maps in YOLO Architecture
## 1 Introduction  
The expected population increase from 8.2 billion in 2024 to a peak population of 10.3 billion in the mid-2080s [1] drives an increased demand for food, which in turn, results in the need for crop expansion. Nevertheless, crop production is affected by various factors such as crop pests and pollinators decline. According to [2], due to pests and insects, there is an estimated crop production loss of 18-20% worldwide. On the other hand, the decline of pollinators is an increasingly alarming problem for farmers. Pollinators support the seed production of 87 of the principal food crops globally, emphasizing the critical need for pollinator conservation, especially since approximately 40% of pollinators, such as bees and butterflies, are at risk of extinction [3]. Thus, effective insect management plays a crucial role in reducing crop losses, helping to maintain healthy and productive harvests. By adequately managing insect populations, farmers can mitigate the damagecaused by pests while simultaneously promoting the vital role of pollinators. This balanced approach can not only reduce crop losses, but also enhances crop yield quality, thereby increasing its market value [4]. Effective management is essential in both pest control and the conservation of pollinators, which are integral to the reproduction of many crops, ensuring long-term agricultural productivity and environmental health [5].  
Traditionally, insect management has relied on human resources. However, this approach is not a practical solution for large-scale crops, as it requires a high level of expertise and is an arduous task that demands considerable time. Therefore, automated methods are essential for addressing these limitations [6].  
Machine learning (ML) has been one of the efforts to automate this task. Traditional ML techniques, such as support vector machines and k-nearest neighbors, had been effectively applied across a wide variety of areas, such as crop yield prediction [7], breast cancer diagnosis [8], solar radiation forecasting [9], protein function prediction [10], among others. However, these methods depend on manually engineered features, whose design often requires certain expertise and can be time-consuming to extract [11]. Furthermore, these manually crafted features often fail to capture the representation of the wide-range, large-scale variations in object shapes, such as the vast diversity of insect species, shapes, and sizes [12], which consequently diminishes the performance of ML techniques.  
To overcome traditional ML limitations, convolutional neural networks (CNN) have proven to be a powerful alternative. Unlike traditional ML approaches, CNNs can automatically learn hierarchical feature representations from raw image data, eliminating the need for manual feature extraction [11]. This ability makes CNNs particularly suitable for tasks involving complex visual patterns, such as insect detection, where variations in species, shapes and orientations pose significant challenges. CNN-based models have demonstrated remarkable success in various computer vision applications such as image classification [13; 14], object detection [15; 16] and image segmentation [17; 18], enabling more accurate and robust object recognition. Nonetheless, insect detection remains particularly challenging due to their small size, often occupying only a tiny fraction of the pixels in an image, which limits the visual information for CNNs to extract meaningful features.  
This has motivated extensive research on improving CNN based models for small object detection, particularly in the context of insect recognition, which remains as an active research area. Next, we provide an overview of related work, highlighting key advancements in this field.