MTMS300: a multiple-targets and multiple-scales benchmark dataset for salient object detection

During the development of salient object detection algorithms, benchmark datasets have played a critical role. However, existing benchmark datasets commonly suffer from dataset bias, making it challenging to fully reflect the performance of different algorithms or capture the technical characteristics of certain typical applications. To address these limitations, we have undertaken two key initiatives: (1) We designed a new benchmark dataset, MTMS300 (Multiple Targets and Multiple Scales), tailored to reconnaissance and surveillance applications. This dataset contains 300 color visible-light images from land, sea, and air scenarios, featuring: Reduced center bias, Balanced distribution of target-to-image area ratios, Diverse image sizes, Multiple targets per image.(2) We curated a new benchmark dataset, DSC (Difficult Scenes in Common), by identifying images from publicly available benchmarks that pose significant challenges (with low metric scores) for most non-deep-learning algorithms. The proposed datasets exhibit distinct characteristics, enabling more comprehensive evaluation of visual saliency algorithms. This advancement will drive the development of visual saliency algorithms toward task-specific applications.