{Chun-Yi Lee Chien-Yao Wang Yu-Cheng Xia Yu-Chen Huang En-Ming Huang Chia-Chi Hsu Mu-Yi Shen Hao-Yu Hou}

Abstract

Detecting small objects is often impeded by blurriness and low resolution, which poses substantial challenges for accurately detecting and localizing such objects. In addition, conventional feature extraction methods usually face difficulties in capturing effective representations for these entities, as down-sampling and convolutional operations contribute to the blurring of small object details. To tackle these challenges, this study introduces an approach for detecting tiny objects through ensemble fusion, which leverages the advantages of multiple diverse model variants and combines their predictions. Experimental results reveal that the proposed method effectively harnesses the strengths of each model via ensemble fusion, leading to enhanced accuracy and robustness in small object detection. Our model achieves the highest score of 0.776 in terms of average precision (AP) at an IoU threshold of 0.5 in the MVA Challenge on Small Object Detection for Birds.