A coarse to fine framework for recognizing and locating multiple diatoms with highly complex backgrounds in forensic investigation

In the forensic investigation, recognizing and locating the multiple diatom objects in an image is a challenging issue due to the interferences of the highly complex backgrounds. To address this issue, a coarse to fine diatom recognition and a localization framework based on the deep learning network is proposed in this paper. Firstly, the diatom images are obtained by performing the anatomic study on the cadavers. Next, a high definition electron microscope is scanned. Then, a coarse to fine deep learning framework is constructed to locate and recognize the diatom objects. Unlike the existing diatom classification and recognition methods, which used light microscopy with low resolution and completed under a simple backgrounds, our framework utilizes the high definition electron scanning microscopy with much higher resolution and suffers from the complex backgrounds interferences. To demonstrate the effectiveness of the proposed framework, 4 diatom image datasets with different background interference degrees are constructed. Also, 3 computer numerical simulation analysis are performed. They are (1) the limitations of the traditional methods in the diatom recognition, (2) the optimized composition of the training strategies and the network models, and (3) the performance of the proposed framework. The computer numerical simulation results show that the proposed framework achieves a recognition accuracy of 0.852. This is greater than 0.758 achieved by the AlexNet. Moreover, it can overcome the problem of the highly complex backgrounds interferences in the forensic investigation. Furthermore, it can locate and recognize the multiple objects in various diatom images simultaneously.