Deep Embedded Lightweight CNN Network for Indoor Objects Detection on FPGA

Abstract indoor objects detection and recognition present an active research axis in computer vision and artificial intelligence fields. Various deep learning-based techniques can be applied to solve the objects detection problems. With the apperance of deep convolutional neural networks (DCNN) a great breakthrough for various applications was achieved. Indoor objects detection present a primary task that can assist blind and visually impaired persons (BVI) during their navigation. However, building a reliable indoor objects detection system used for edge devices implementations still presents a serious challenge. To address this problem, we propose in this work to build an indoor objects detection system based on DCNN network. Cross-stage partial network (CSPNet) has been used for the detection process and a lightweight backbone based on EfficientNet v2was used as a network backbone. In order to ensure a lightweight implementation of the proposed work on FPGA devices, various optimizations techniques have been applied to compress the model size and reduce its computation complexity. The proposed indoor objects detection system was implemented on a Xilinx ZCU 102 board. Training and testing experiments have been conducted on the proposed indoor objects dataset that count 11000 images containing 25 landmark classes. The proposed work achieved very competitive results in terms of detection accuracy and processing time for the original CSP-EfficientNet v2 network as well as for the proposed compressed version.