## 2 Related Studies  
Detecting birds that fly across the sky in three-dimensional space is a challenging task for researchers and developers. Over a half-century ago, bird detection using radar [4] was studied to prevent bird strikes by aircraft at airports. By virtue of advances in software technology and improvements in the performance and affordability of sensors and computers, bird detection methods have diversified, especially in terms of offering improved accuracy and cost-effective approaches. Recent bird-detection methods can be categorized into two major modalities based on survey articles: visual [5; 6; 7; 8] and audio [9; 10; 11]. Numerous outstanding methods [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49] have been proposed from both modalities. Benchmark datasets and challenges for the verification and comparison of detection performance have been expanded in both vision [50; 51; 52; 53] and acoustic [54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65] modalities. During the first half of the 2010s, probability models and conventional machine-learning (ML) models combined with part-based features obtained using local descriptors were used widely and popularly. These methods comprise \(k\)-means [66], morphological filtering (MF) [67], principal component analysis (PCA), Gaussian mixture models (GMMs),  
Figure 1: Mallards for rice–duck farming (**left**). After some growth of rice plants (**right**), mallards are hidden by the plants. Detecting mallards visually would be extremely difficult. expectation-maximization (EM) algorithms [68], boosting [69], scale-invariant feature transform (SIFT) [70], random forests (RFs) [71], bag-of-features (BoF) [72], histograms of oriented gradients (HOGs) [73], support vector machines (SVMs) [74], local binary patterns (LBPs) [75], background subtraction (BS) [76], and multi-instance, multi-label (MIML) [77]. These methods require preprocessing of input signals for the enhancement of the features. Moreover, algorithm selection and parameter optimizations conducted in advance for preprocessing have often relied on the subjectivity and experience of the developers. If data characteristics differ even slightly, then performance and accuracy drop drastically. Therefore, parameter calibration is necessary and represents a great deal of work. In the latter half of the 2010s, deep-learning (DL) algorithms have been predominant, especially after the implementation of the error back-propagation learning algorithm [78] in convolutional neural networks (CNNs) [79]. Tables 1 and 2 respectively present representative studies of vision-based and sound-based bird detection methods reported during the last decade. As presented in the fourth columns, the representative networks and backbones used in bird detection methods are the following: regions with CNN (RCNN) [80], VGGNet [81], Inception [82], ResNet [83], XNOR-Net [84], densely connected convolutional neural networks (DC-CNNs) [85], fast RCNN [86], faster RCNN [87], you only look once (YOLO) [88], and weakly supervised data augmentation network (WS-DAN) [89]. End-to-end DL models require no pre-processing for feature extraction of the input signals [90]. Moreover, one-dimensional acoustic signals can be input to the DL model as two-dimensional images [91].