Mish-BiFPN: Temperature-Controlled Mish Activation With Bidirectional Feature Pyramid Networks for Thermal Hotspot Detection and Energy Conservation in Photovoltaic Panels

The current level of adoption of solar photovoltaic systems requires better methods of thermal fault monitoring, which will support sustainable energy production systems. The application of traditional methods in detecting hotspots is highly significant in analysing the thermal anomaly pattern, but the procedure is ineffective since the process of inspection is manual, time-consuming, and labour-intensive. However, thermal imaging devices offer scalable solutions that are limited in terms of managing complex thermal patterns and combining temperature data at various scales. This paper proposes a Mish-BiFPN Framework to detect thermal hotspots in solar PV systems. Unlike conventional BiFPN or Mish-based models, the proposed framework integrates a temperature-controlled Mish activation with CBAM-guided fusion at every pyramid level, combined with a RegNet backbone and thermal-preserving preprocessing to enhance sensitivity to subtle heat gradients. The offered architecture uses multi-scale feature extraction and attention layers in a way that the model can focus on temperature-sensitive and spatial features at different pyramid layers. It ensures thermal characteristics are retained through preprocessing and data augmentation. The framework further provides a basis to combine the real-time thermal performance monitoring with long-term energy planning that allows dynamically calibrating estimates of solar PV energy generation. This model is trained and validated on thermal hotspot datasets, achieving 12 percentage-point improvement in precision, a 6% improvement in recall, 9% in F1-score, and 21% in Intersection over Union in comparison to the YOLOv10 model. The model shows its potential to address complicated thermal patterns. Energy loss modeling demonstrates that single hotspot detection can prevent approximately 9 kWh per year of power losses per 450 W module. Therefore, this model is very appropriate for large-scale solar PV systems, with both accurate detection and energy conservation optimization.