Automated diatom detection in forensic drowning diagnosis using a single shot multibox detector with plump receptive field

The detected result of diatoms is an important indicator in forensic drowning examination, and most of the current deep learning methods have achieved greater success in detecting diatoms with simple or no backgrounds. However, diatom images captured by the high-definition electron scanning microscopy in modern forensic science contain complex backgrounds and hamper the accurate diatom detection, resulting in the omission detection of the small and marginal diatoms in multi-diatom scenario. In this paper, we proposed a Hybrid-Dilated-Convolution-incorporated Single Shot Multibox Detector (HDC-SSD) to address this problem. By adopting the merit of the plump receptive field of HDC, the proposed algorithm not only improves the detection rate but also enhances the detection ability of the small objects and the marginal objects. The proposed method was validated by using our self-established dataset. Compared with SSD, the HDC-SSD reduces the undetected rate by approximately 48.6% and almost keeps as fast as the SSD. More importantly, compared with some current state-of-the-art methods, the HDC-SSD obtains the highest Recall value at 0.9302.