Pedestrian detection is a crucial component for intelligent transport system and advanced driver assistance system. In recent years, pedestrian detection methods have achieved higher accuracy. However, the existing algorithms are insufficient for small-scale pedestrian detection that is relatively far from cameras in practical applications. In this paper, we propose a novel deep small-scale sense network (termed SSN) for small-scale pedestrian detection. The proposed architecture could generate some proposal regions which are more effective to detect small-scale pedestrians. Furthermore, we design a novel loss function based on cross entropy loss to increase the loss contribution from hard-to-detect small-scale pedestrians. In addition, a novel evaluation metric is introduced, which can measure the location precision of the pedestrian detection methods. In addition an Asian pedestrian detection dataset named VIP pedestrian dataset is constructed from various road condition data. Our method achieves good detection performance on Caltech pedestrian dataset and our VIP pedestrian dataset.

Ships comprise the only and most important ocean transportation mode. Thus, ship detection is one of the most critical technologies in ship monitoring, which plays an essential role in maintaining marine safety. Optical remote-sensing images contain rich color and texture information, which is beneficial to ship detection. However, few optical remote-sensing datasets are open publicly due to the issue of sensitive data and copyrights, and only the HRSC2016 dataset is built for the ship-detection task. Moreover, almost all general object detectors suffer from the failure of multi-scale ship detection because of the diversity of spatial resolution and ship size. In this paper, we re-annotate the HRSC2016 dataset and supplement 610 optical remote-sensing images to build a new open source ship-detection benchmark dataset with rich multi-scale ship objects named the HRSC2016-MS dataset. In addition, we further explore the potential of a recursive mechanism in the field of object detection and propose a novel multi-scale ship-detection framework (MSSDet) in optical remote-sensing images. The success of detecting multi-scale objects depends on the hierarchical pyramid structure in the object-detection framework. However, the inherent semantic and spatial gaps among hierarchical pyramid levels seriously affect detection performance. To alleviate this problem, we propose a joint recursive feature pyramid (JRFP), which can generate semantically strong and spatially refined multi-scale features. Extensive experiments were conducted on the HRSC2016-MS, HRSC2016, and DIOR datasets. Detailed ablation studies directly demonstrated the effectiveness of the proposed JRFP architecture and also showed that the proposed method has excellent generalizability. Comparisons with state-of-the-art methods showed that the proposed method achieves competitive performance, i.e., 77.3%, 95.8%, and 73.3% mean average precision accuracy on the HRSC2016-MS, HRSC2016, and DIOR datasets, respectively.