DARE-YOLO: A Lightweight Object Detection Algorithm and Its FPGA Acceleration for Sustainable PV Panel Inspection

As a critical component of sustainable energy systems, the efficient maintenance of photovoltaic (PV) panels is essential. While deep learning is an important approach for PV panel defect detection, the high complexity of existing models and their substantial computational demand make deployment on edge platforms difficult. This paper studies an acceleration method for photovoltaic panel defect detection on the Zynq-7020 heterogeneous platform. We design DARE-YOLO, a lightweight network for photovoltaic panel defect detection, together with a Zynq-based accelerator. In DARE-YOLO, we introduce RepConv and a lightweight single-path backbone to reduce the memory bandwidth overhead caused by multi-branch structures. We further design a Dilated Context Block (DCB) and a Dual-scale Decoupled Head (DDH), which effectively improve the detection accuracy of DARE-YOLO. On the Zynq platform, we develop the accelerator through a mixed fixed-point quantization strategy, a custom convolution IP core, and pipeline unrolling. These optimizations reduce data access latency, improve computational parallelism, and increase computational throughput. Experimental results show that DARE-YOLO achieves 93.84% mAP@0.5 with only 6.4 M parameters. The accelerator has a total on-board power consumption of only 1.95 W, while delivering a throughput of 37.5 GOPS, an energy efficiency of 19.23 GOPS/W. The image inference latency is 661.3 ms. This low-power, high-efficiency co-design paradigm ensures the long-term reliability of renewable energy facilities.