Detection of moving objects in multi-complex environments using selective attention networks (SANet).

Object detection studies aim to solve safety problems at industrial sites; however, improving object detection performance and ensuring real-time capabilities simultaneously remains challenging in multi-complex industrial environments. This paper proposes an unconditionally protected detector to address this problem. The detector's backbone network uses a residual block with a bottleneck structure to reduce computation and enhance real-time performance. Additionally, a selective attention network enhances object detection by extracting important features based on the morphological characteristics of the feature map in the neck structure. The method includes a whole-body estimation to locate individuals obstructed by obstacles, thereby preventing collisions with vehicles and avoiding hazardous situations in restricted areas. The proposed method enables real-time object detection and risk assessment for nearby objects, enhancing safety for industrial vehicle drivers and workers. Moreover, it can lead to further improvements in object detection performance in research fields such as autonomous driving and AI CCTV utilizing cameras. • Build dataset of industrial sites and improve the object detection performance on various complex backgrounds • Deep learning-based object detection method for accident prevention in industrial and construction environments • Unconditionally protected detector based on a selective attention network to extract and strengthen important features • Application of various model optimization techniques to shorten learning time and achieve efficient learning • Whole-body estimation method to detect location of an actual person to prevent dangerous situations at industrial sites [ABSTRACT FROM AUTHOR]