## 5 Results and Discussion
### Mitigating Adverse Visual Conditions  
Computer vision systems are challenged by low-contrast images, shadows, or adverse weather. Several research works have been conducted to mitigate the effects of adverse visual conditions on camera sensing and subsequent object detection. Deep learning has shown impressive performance in low-light image enhancement. Techniques, such as multiscale [50, 51] and attention feature maps [52], have been successfully utilized. An end-to-end attention-based multi-branch CNN was developed in [53]. It performs denoising and low-light enhancement simultaneously to deal effectively with colour distortion and noise that are also occurring within dark images. Consisting of four subnetworks, the system creates an under-exposed attention map to handle the under exposed image parts and then the noise map is derived. Both image representations are further used in a multi-branched subnetwork for the image enhancement task. A final separate CNN is utilized for contrast, exposure, and colour improvement. The proposed model outperformed other state-of-the-art methods in terms of enhancement rate, with testing time reported as 0.05 s for the lightweight model version and 0.48 s for the model tested on the SID dataset. Apart from low-light image enhancement, rainy images can also be effectively derained [54, 55] or defogged [56, 57]. The high-quality image enhancement techniques can be used as a pre-processing step to improve object/pothole detection. However, the computational constraints and overall inference speed should be considered. Another approach is to perform enhancement and detection jointly in an end-to-end manner. According to [58], a learnable pre-processing module for low-light image enhancement may decrease the accuracy of detection in some cases. The authors of [58] proposed learnable low-light image enhancement implemented jointly with the detection task utilizing the twin architecture. By using the information at both the original and enhanced features levels, an improvement in face detection was achieved. In [59], an image-adaptive YOLO deals with weather-specific information using a fully differentiable image processing module (DIP) to pre-process high-resolution images for input to YOLOv3. Hyperparameters of the DIP module are learned by separate the CNN predictor networks. The proposed system performed effectively under foggy and low-light scenarios with an inference time increase of 13 ms over the YOLOv3 baseline. Improving internal modules of existing object detection architecture may increase model robustness and allow for better regulation of the model speed. A Trans-Decoupled YOLO [60], which is designed for small object detection in complex environments, integrates transformer modules with a self-attention mechanism into the YOLOv5 backbone for global contextual feature extraction. Additionally, a decoupled lightweight head for both simplified and more accurate detection was proposed. Improvements of 6.4% (mAP@.5) and 6.8% (mAP@ [0.5:0.95]) over YOLOv5 on the TT100K dataset were reported. Convolutional Block Attention Module (CBAM) is another visual attention-based module used to improve not only the small object detection but also to mitigate adverse weather conditions [61, 62]. Inspired by CBAM, the authors of [63] proposed a global attention mechanism with 3D permutation in its channel part and increased number of conv layers in the spatial part. A common approach to improve a model's generalization to adverse conditions is to incorporate images captured under different weather and lighting conditions in the training dataset [9, 10]. The available data can also be augmented with synthetically created images or with original images translated into different conditions. For instance, synthetically produced rain data added to training images improved object detection by 21% in [64]. The influence of adverse visual conditions can also be mitigated by the fusion of different sensors.